Method for testing lithium battery of dust collector

Abstract

The invention relates to the technical field of battery performance testing, in particular to a dust collector lithium battery testing method which comprises the steps that a lithium battery pack in a dust collector is charged and discharged for multiple times, and testing data of each testing parameter of each lithium battery in the lithium battery pack at each moment are obtained through an intelligent sensor; determining the contact abnormity degree of the lithium battery pack at each moment; obtaining an effective test time period and a deviation anomaly degree; obtaining an effective data set of each test parameter of each lithium battery; determining a 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 dust collector. According to the invention, the phenomena of contingency and singleness of the test result are avoided, so that the performance test of the lithium battery is more reliable, and the accuracy of the performance test of the lithium battery is improved.

Description

Technical Field

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

[0002] As a new type of chemical energy, 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 promptly 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 energy density, service life, self-discharge rate, charging efficiency and internal resistance of the lithium battery are normal. Secondly, in a vacuum cleaner, a group of lithium batteries usually work together to maintain the normal operation of the vacuum cleaner. When the voltage, internal resistance, capacity and other parameters of different lithium batteries in the lithium battery pack differ greatly, the loss of the lithium battery pack will increase. Therefore, it is necessary to conduct consistency testing on the changes in battery parameters during the charging and discharging process of the lithium battery pack. When performing consistency testing on the lithium battery pack of a vacuum cleaner, common testing methods include internal resistance measurement, capacity measurement and voltage measurement. 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 it is fully charged, which makes the test results have errors and are accidental and single, resulting in low accuracy in the performance test of lithium batteries. Summary of the invention

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

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

[0006] The lithium battery pack in the vacuum cleaner is charged and discharged multiple times, and the test data of each test parameter of each lithium battery in the lithium battery pack at each time is obtained through the intelligent sensor;

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

[0008] Based on the contact abnormality, an effective test period is obtained; by analyzing the deviation of the test data of each test parameter of each lithium battery at each moment in the effective test period, the deviation abnormality of each lithium battery at each moment in the effective test period is obtained;

[0009] Based on the deviation abnormality, the test data at all times within the effective test period are eliminated for abnormalities, and a valid data set for each test parameter of each lithium battery is obtained; the correlation between the valid data sets between different lithium batteries under each test parameter is analyzed, and the consistency factor of each test parameter of the lithium battery pack is determined; based on the consistency factor of all test parameters of the lithium battery pack, the consistency coefficient of the lithium battery pack is determined, and the performance of the lithium battery pack of the vacuum cleaner is tested.

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

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

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

[0013] Count the frequency of 0 values ​​appearing in the test data of all moments after 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 record it as the relative difference;

[0015] The cumulative sum of the abnormal characterization degree, the frequency and the relative difference of all lithium batteries under each test parameter at each time is used as the poor contact coefficient of each test parameter of the lithium battery pack at each time;

[0016] The normalized result of the sum of the poor contact coefficients of all test parameters at each moment is taken 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 time comprises:

[0018] Calculate the discrete degree of the test data of each test parameter of each lithium battery at multiple moments before each moment, and record it as the previous stage discrete degree;

[0019] Calculate the discrete degree of the test data of each test parameter of each lithium battery at multiple moments after each moment, and record it as the latter-stage discrete degree;

[0020] The difference between the latter discreteness and the former discreteness is calculated and recorded as discrete difference. The calculation result of the exponential function with the natural constant as the base and the discrete difference as the exponent is used as the abnormal characterization degree of each test parameter of each lithium battery at each moment.

[0021] Preferably, obtaining the effective test period includes: recording the moment when the contact abnormality 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 poor contact initial moment, and recording all moments before the poor contact initial moment as the effective test period.

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

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

[0024] The sum of the Z scores of the test data corresponding to all the test parameters of each lithium battery at each time within the effective test period is recorded as the relative deviation coefficient;

[0025] Count the number of test data corresponding to all test parameters of each lithium battery at each time in the effective test period whose absolute values ​​are greater than a preset second threshold, and record it as the number of outliers;

[0026] The product of the relative deviation coefficient and the number of outliers is used as the deviation abnormality of each lithium battery at each moment in the effective test period.

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

[0028] Using the threshold segmentation algorithm, the deviation abnormality of each lithium battery at all times during the effective test period is segmented to obtain the segmentation threshold;

[0029] The moment when the deviation abnormality is greater than the segmentation threshold is recorded as the abnormal moment; the test data of all the abnormal moments of each test parameter of each lithium battery in the effective test period are eliminated, and the test data of all other moments of each test parameter of each lithium battery in the effective test period constitute a valid data set of each test parameter of each lithium battery.

[0030] Preferably, the consistency factor of each test parameter of the lithium battery pack is the average of the correlation degrees of the valid 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: Among them, α r is the preset parameter weight corresponding to the rth test parameter of the lithium battery pack, U ris the consistency factor of the rth test parameter of the lithium battery pack, and R is the number of all test parameters of the lithium battery pack.

[0032] Preferably, the detecting 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.

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

[0034] The present application analyzes 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 combines the abnormal characterization degree to determine the contact abnormality of the lithium battery pack at each moment. The beneficial effect is that it takes into account the degree of fluctuation difference of the test data corresponding to different moments before and after each moment to reflect the possible situation of poor contact of the sensor or tester corresponding to each moment, analyzes the degree of abnormality of the test data after each moment, and each test parameter of each lithium battery The difference in the test data of the parameters between adjacent moments is used to reflect the possibility of poor contact of the lithium battery pack at the corresponding moment; based on the contact abnormality, an effective test period is obtained, which has the beneficial effect of considering the moment when the poor contact first occurs, and screening out the test data collected before the moment of poor contact, so as to perform abnormality 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 in the effective test period, the deviation abnormality of each lithium battery at each moment in the effective test period is obtained, which has the beneficial effect of considering The possibility of abnormal test data at each moment in the effective test period is determined to reflect the situation that the test data of the lithium battery at the corresponding moment is subject to electromagnetic interference from the environment, indicating the possibility of error in the test data at the corresponding moment; based on the deviation abnormality, the test data at all moments in the effective test period are abnormally eliminated to obtain a valid data set for each test parameter of each lithium battery, which has the beneficial effect of eliminating abnormal test data, thereby improving the accuracy of the performance test of the lithium battery; analyzing the correlation between the effective data sets of different lithium batteries under each test parameter, and determining the consistency of each test parameter of the lithium battery group Factor; based on the consistency factor of all test parameters of the lithium battery pack, the consistency coefficient of the lithium battery pack is determined, and the performance of the lithium battery pack of the vacuum cleaner is tested. The beneficial effect is that the similarity of the test data between different lithium batteries under each test parameter is taken into account to reflect the consistency of the change trend of the test data of each test parameter of all lithium batteries in the lithium battery pack, and the performance of the lithium battery pack is described. By analyzing multiple test parameters of the lithium battery pack during multiple charge and discharge processes, the phenomenon of randomness and singularity in the test results is avoided, so that the performance test of the lithium battery is more reliable and the accuracy of the performance test of the lithium battery is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] A vacuum cleaner lithium battery testing method of the present application is further described in detail below with reference to the accompanying drawings.

[0036] Figure 1A flowchart of a method for testing a lithium battery for a vacuum cleaner provided in an embodiment of the present application;

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

[0038] Figure 3 A flowchart of the steps of a method for obtaining the deviation abnormality of each lithium battery at each moment within the effective test period provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the present application more clear, the following is a further detailed description of a vacuum cleaner lithium battery testing method proposed in the present application in combination with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] See also Figure 1 , which shows a flow chart of a method for testing a vacuum cleaner lithium battery provided by an embodiment of the present application, the method comprising the following steps:

[0042] Step 1: Perform multiple charge and discharge processes on the lithium battery pack in the vacuum cleaner to obtain test data of each test parameter of each lithium battery in the lithium battery pack at each time.

[0043] The lithium battery pack in the vacuum cleaner is subjected to multiple charge and discharge processes, and the test data of each test parameter of each lithium battery in the lithium battery pack during the multiple charge and discharge processes are collected through intelligent sensors, wherein the intelligent sensors include voltage sensors, current sensors, temperature sensors, battery capacity testers and internal resistance testers, and 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, wherein each test parameter includes: voltage, current, temperature, capacity, internal resistance, and all the collected data are normalized.

[0044] In this embodiment, the number of lithium batteries in the lithium battery pack of the vacuum cleaner is 8, and the lithium battery pack in the vacuum cleaner is charged and discharged 10 times, and the collection time interval is 1 minute. As other implementation modes, the implementer can set it according to the actual situation; secondly, the maximum and minimum normalization method is used for normalization processing, among which the maximum and minimum normalization method is a well-known technology and will not be repeated here. As other implementation modes, the implementer can adopt other methods of the prior art, such as the Z-score normalization method, etc., and this embodiment does not impose any special restrictions on this.

[0045] At this point, the test data of each test parameter of each lithium battery in the lithium battery pack of the vacuum cleaner at each time is obtained.

[0046] Step 2, analyze the fluctuation difference of each test parameter of each lithium battery before and after each moment, the difference of test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of test data after each moment, and determine the contact abnormality degree of the lithium battery pack at each moment in combination with the abnormal characterization degree.

[0047] Inside the vacuum cleaner, generally multiple lithium batteries are connected in series and parallel to make the vacuum cleaner work properly. If the voltage, resistance and other test parameters between the lithium batteries are inconsistent, it will cause certain losses to the lithium battery pack. Therefore, when testing lithium batteries, it is not enough to just test the performance of a single lithium battery, but also necessary to test 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 time, 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 time, the worse the consistency of the lithium battery pack.

[0048] However, in the process of collecting each test parameter, there will be a certain degree of interference, such as electromagnetic interference caused by household appliances to the sensor, or poor contact between the sensor and the lithium battery caused by the vibration of the vacuum cleaner, which may result in the collected test data not being the actual data of the lithium battery, and there will be a certain deviation in the test of the lithium battery. Therefore, before performing consistency analysis on 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 sensors, each test parameter of each lithium battery will be offset and changed at the same time, and the change may be larger or smaller; when the vacuum cleaner vibrates to a large extent during operation, it may cause poor contact between multiple sensors and lithium batteries. In this case, the measurement values ​​of one or more sensors will suddenly change at the same time, and the data after the mutation point will be significantly different from the data before the mutation point, among which the measurement values ​​after the mutation point will become more chaotic and there may be a moment when the measurement value is zero; when the sensor is neither subject to 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 no mutation points will appear. Based on the above analysis, the contact abnormality is determined. The step flow chart of the method for obtaining the contact abnormality of the lithium battery pack at each moment provided in the embodiment of the present application is as follows 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, the poor contact coefficient is determined 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 discrete degree of the test data of each test parameter of each lithium battery at multiple moments before each moment, and record it as the previous stage discrete degree;

[0052] Calculate the discrete degree of the test data of each test parameter of each lithium battery at multiple moments after each moment, and record it as the latter-stage discrete degree;

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

[0054] Calculate the difference between the latter-stage discreteness and the former-stage discreteness, record it as discrete difference, and use the calculation result of the exponential function with the natural constant as the base and the discrete difference as the exponent as the abnormality characterization degree of each test parameter of each lithium battery at each moment;

[0055] In this embodiment, the difference between the latter discreteness and the former discreteness is calculated and recorded as discrete difference; secondly, the formula for the abnormal characterization degree of each test parameter of each lithium battery at each time is: A i,r,t =exp(vh i,r,t -vq i,r,t ), where A i,r,t is the abnormal characterization degree of the rth test parameter of the i-th lithium battery at the tth time, vh i,r,t is the dispersion of the rth test parameter of the ith lithium battery at the tth moment, vq i,r,t is the dispersion of the rth test parameter of the ith lithium battery at the tth time, exp() is an exponential function with a natural constant as the base, and secondly, vh i,r,t -vq i,r,t is the discrete difference.

[0056] Count the frequency of 0 values ​​appearing in the test data of all moments after 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 record it as the relative difference;

[0058] In this embodiment, the absolute value of the difference between the test data of each test parameter of each lithium battery at each moment and the test data at the previous moment is calculated and recorded as the relative difference.

[0059] The cumulative sum of the abnormal characterization degree, the frequency and the relative difference of all lithium batteries under each test parameter at each time is used as the poor contact coefficient of each test parameter of the lithium battery pack at each time;

[0060] In this embodiment, the calculation method of the poor contact coefficient of each test parameter of the lithium battery pack at each time is: Among them, P r,t A is the poor contact coefficient of the rth test parameter of the lithium battery pack at the tth moment, i,r,t is the abnormality representation degree of the rth test parameter of the i-th lithium battery at the tth time, sh i,r,t is the frequency of the occurrence of 0 value in the test data of the rth test parameter of the i-th lithium battery at all times after the tth time, Δu i,r,t is the relative difference of the rth test parameter of the ith lithium battery at the tth 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 abnormal characterization degree A i,r,tThe larger the value, the greater the fluctuation of the test data of the rth test parameter of the i-th lithium battery after the tth moment is than that of the test data before the tth moment, indicating that the possibility of poor contact between the sensor or tester corresponding to the i-th lithium battery and the rth test parameter at the tth moment is greater; when the frequency sh i,r,t The larger the value, the higher the frequency of zero value of the i-th lithium battery after the t-th time. Therefore, the possibility of poor contact of the sensor or tester corresponding to the r-th test parameter of the i-th lithium battery at the t-th time is greater. The relative difference Δu i,r,t The larger the value, the greater the difference between the test data of the rth test parameter of the i-th lithium battery at the tth time and the test data at the t-1th time. In this case, poor contact may have occurred at the tth time. The poor contact coefficient P r,t The larger it is, the greater the possibility that the sensor or tester corresponding to the rth test parameter at the tth time has poor contact with the lithium battery.

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

[0063] Normalizing 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, wherein the sigmoid function is a well-known technology and will not be described in detail here. As other implementation methods, the implementer may adopt other methods of the prior art, such as the tanh function, etc. This embodiment does not impose any special restrictions on this.

[0065] It should be noted that, the greater the contact abnormality, the greater the possibility that the sensor or tester has poor contact with the lithium battery at the corresponding moment.

[0066] Thus, the contact abnormality degree of the lithium battery pack at each time is obtained.

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

[0068] Further, based on the contact abnormality, it is determined whether the contact between the sensor or the tester and the lithium battery is abnormal, and an effective test period is determined, specifically:

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

[0070] All the time before the initial time of poor contact is recorded as the effective test period;

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

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

[0073] Further, the abnormal conditions of the test data at all times within the effective test period are analyzed, specifically:

[0074] Using the Z-score algorithm, calculate the Z score of the test data of each test parameter of each lithium battery at each time within the effective test period;

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

[0076] Count the number of test data corresponding to all test parameters of each lithium battery at each time in the effective test period whose absolute values ​​are greater than a preset second threshold, and record it as the number of outliers;

[0077] In this embodiment, the preset second threshold value is 3, wherein the Z-score algorithm is a well-known technology and is not described in detail here. The Z score Z corresponding to the test data at the tth time in the effective test period is t The calculation formula is: x t is the test data at the tth moment in the effective test period, μ is the mean of the test data at all moments in the effective test period, σ is the standard deviation of the test data at all moments in the effective test period, wherein the preset second threshold is 3, which is set according to the Laida criterion, that is, the statistical x t The number of values ​​not distributed in the range (μ-3σ,μ+3σ), if x t The value of is not distributed in the range of (μ-3σ,μ+3σ), that is, Z t The absolute value of is greater than 3, indicating that the test data at the corresponding moment is an abnormal point. Among them, the Laida criterion is a well-known technology and will not be described here.

[0078] The product of the relative deviation coefficient and the number of outliers is used as the deviation abnormality of each lithium battery at each moment in the effective test period;

[0079] It should be noted that the larger the relative deviation coefficient is, the greater the deviation of the test data of all test parameters of each lithium battery at the corresponding moment compared with the overall data is, reflecting that the test data of each lithium battery at the corresponding moment is more likely to be affected by the electromagnetic interference of the environment, and the test data at this moment is more likely to have errors; the larger the number of outliers is, it means that the test data of multiple sensors at the corresponding moment have produced a large deviation, which further indicates that environmental interference may have occurred at the corresponding moment, and the larger the obtained deviation abnormality is, the greater the possibility that the test data of the lithium battery group 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 follows Figure 3 shown.

[0080] So far, the deviation abnormality of each lithium battery at each moment in the effective test period is obtained.

[0081] Step 4, based on the deviation abnormality, the test data at all times in the effective test period are eliminated to obtain a valid data set for each test parameter of each lithium battery; the correlation between the valid data sets between different lithium batteries under each test parameter is analyzed to determine the consistency factor of each test parameter of the lithium battery pack; based on the consistency factor of all test parameters of the lithium battery pack, the consistency coefficient of the lithium battery pack is determined to detect the performance of the lithium battery pack of the vacuum cleaner.

[0082] Further, based on the deviation abnormality, the abnormal data of each test parameter of each lithium battery in the test data at all times within the effective test period are eliminated, specifically:

[0083] Using a threshold segmentation algorithm, performing threshold segmentation on the deviation abnormality of each lithium battery at all times within the effective test period to obtain a segmentation threshold;

[0084] The moment when the deviation abnormality is greater than the segmentation threshold is recorded as an abnormal moment;

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

[0086] Eliminate the test data of all abnormal moments of each test parameter of each lithium battery within the effective test period, and use the test data of all other moments of each test parameter of each lithium battery within the effective test period to form a valid data set of each test parameter of each lithium battery;

[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, 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 may 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 consistency coefficient obtained, the higher the performance consistency of each lithium battery in the lithium battery pack in the vacuum cleaner during operation, which reflects that the battery performance of the lithium battery pack is better.

[0095] Further, based on the consistency coefficient, the performance of the lithium battery pack of the vacuum cleaner is tested, specifically:

[0096] If the consistency coefficient is greater than the preset third threshold value, 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 third threshold value is preset to be 0.6. As other implementation modes, the implementer may set the value according to the actual situation.

[0098] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0099] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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, they should be considered to be within the scope of this specification.

[0100] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A vacuum cleaner lithium battery testing method, characterized in that: The method comprises the following steps: The lithium battery pack in the vacuum cleaner is charged and discharged multiple times, and the test data of each test parameter of each lithium battery in the lithium battery pack at each time is obtained through the intelligent sensor; Analyze the fluctuation difference of each test parameter of each lithium battery before and after each moment, the difference of test data of all test parameters of each lithium battery at different moments, and the abnormal proportion of test data after each moment, to determine the contact abnormality degree of the lithium battery pack at each moment; Based on the contact abnormality, an effective test period is obtained; by analyzing the deviation of the test data of each test parameter of each lithium battery at each moment in the effective test period, the deviation abnormality of each lithium battery at each moment in the effective test period is obtained; Based on the deviation abnormality, the test data at all times within the effective test period are eliminated for abnormalities, and a valid data set for each test parameter of each lithium battery is obtained; the correlation between the valid data sets between different lithium batteries under each test parameter is analyzed, and the consistency factor of each test parameter of the lithium battery pack is determined; based on the consistency factor of all test parameters of the lithium battery pack, the consistency coefficient of the lithium battery pack is determined, and the performance of the lithium battery pack of the vacuum cleaner is tested.

2. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: Each test parameter includes: voltage, current, temperature, capacity, and internal resistance.

3. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: The step of determining the contact abnormality degree of the lithium battery pack at each moment includes: Analyze the difference in the discrete distribution of each test parameter of each lithium battery before each moment and the test data at multiple moments thereafter, and calculate the abnormal characterization degree of each test parameter of each lithium battery at each moment; Count the frequency of 0 values ​​appearing in the test data of all moments after each test parameter of each lithium battery; Calculate the difference between the test data of each test parameter of each lithium battery at each moment and its adjacent moment, and record it as the relative difference; The cumulative sum of the abnormal characterization degree, the frequency and the relative difference of all lithium batteries under each test parameter at each time is used as the poor contact coefficient of each test parameter of the lithium battery pack at each time; The normalized result of the sum of the poor contact coefficients of all test parameters at each moment is taken as the contact abnormality degree of the lithium battery pack at each moment.

4. A vacuum cleaner lithium battery testing method as claimed in claim 3, characterized in that: The calculation of the abnormality characterization degree of each test parameter of each lithium battery at each time includes: Calculate the discrete degree of the test data of each test parameter of each lithium battery at multiple moments before each moment, and record it as the previous stage discrete degree; Calculate the discrete degree of the test data of each test parameter of each lithium battery at multiple moments after each moment, and record it as the latter-stage discrete degree; The difference between the latter discreteness and the former discreteness is calculated and recorded as discrete difference. The calculation result of the exponential function with the natural constant as the base and the discrete difference as the exponent is used as the abnormal characterization degree of each test parameter of each lithium battery at each moment.

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

6. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: The step of obtaining the deviation abnormality of each lithium battery at each time point in the effective test period includes: Using the Z-score algorithm, calculate the Z score of the test data of each test parameter of each lithium battery at each time within the effective test period; The sum of the Z scores of the test data corresponding to all the test parameters of each lithium battery at each time within the effective test period is recorded as the relative deviation coefficient; Count the number of test data corresponding to all test parameters of each lithium battery at each time in the effective test period whose absolute values ​​are greater than a preset second threshold, and record it as the number of outliers; The product of the relative deviation coefficient and the number of outliers is used as the deviation abnormality of each lithium battery at each moment in the effective test period.

7. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: The method for obtaining the valid data set of each test parameter of each lithium battery is as follows: Using the threshold segmentation algorithm, the deviation abnormality of each lithium battery at all times during the effective test period is segmented to obtain the segmentation threshold; The moment when the deviation abnormality is greater than the segmentation threshold is recorded as an abnormal moment; The test data of all abnormal moments of each test parameter of each lithium battery in the effective test period are eliminated, and the test data of all other moments of each test parameter of each lithium battery in the effective test period constitute a valid data set of each test parameter of each lithium battery.

8. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: The consistency factor of each test parameter of the lithium battery pack is the average of the correlation degrees of the valid data sets of all any two lithium batteries under each test parameter.

9. A vacuum cleaner lithium battery testing method as claimed in claim 1, characterized in that: The calculation method of the consistency coefficient Q of the lithium battery pack is: Among them, 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, and R is the number of all test parameters of the lithium battery pack.

10. A vacuum cleaner lithium battery testing method as claimed in 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 value, 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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