Battery quality detection method

Abstract

The invention provides a battery quality detection method. The battery quality detection method comprises the following steps: selecting test data of which the data concentration ratio is greater than a first threshold value from a plurality of pieces of test data; determining an upper limit curve and a lower limit curve of the selected test data; setting a standard detection interval between the upper limit curve and the lower limit curve; acquiring a battery characteristic waveform of the battery; and comparing the battery characteristic waveform with the standard detection interval to evaluate the quality of the battery.

Description

Technical Field

[0001] This application relates to a method for detecting the quality of a battery, and more particularly to a method for evaluating the quality of a battery by detecting the charge and discharge curves of the battery. Background Art

[0002] In order to determine the quality of a battery, manufacturers will conduct a series of charge and discharge tests on the battery and analyze the voltage and current values of the battery during the charge and discharge process. Taking the charging test of the battery as an example, the charging process of the battery often switches between different charging modes. For example, when the battery voltage is lower than the rated voltage, it can be charged in a constant current mode, and when the battery voltage is close to the rated voltage, it can be switched to a constant voltage mode for charging. Also, as the battery capacity approaches the upper limit, the charging current will gradually decrease. Those with ordinary knowledge in the relevant technical field can understand that the battery voltage and current often have different change amounts at different time points.

[0003] In order to determine whether the charge and discharge behavior of the battery meets expectations, traditionally, an upper limit value of a fixed voltage (or current) can be set, and by detecting whether the voltage (or current) of the battery exceeds the upper limit value, it can be determined whether an abnormal situation occurs. For example, assuming that the battery is charged at a constant voltage or the known maximum charging voltage is 4.2V, then the upper limit value of the battery voltage can be set to 4.5V at this time. Thus, as long as the battery voltage exceeds 4.5V during the charging process, it is considered that the charge and discharge behavior of the battery is abnormal. However, the fixed voltage upper limit value is mostly applicable only to the case where the voltage is relatively stable and the battery voltage is close to the rated voltage. It can be understood that when the battery voltage is far from the rated voltage, using only the upper limit value of the voltage as the condition for determining whether an abnormality occurs is a bit too lenient. In practice, when the battery voltage is far from the rated voltage, usually the change in voltage (or current) is also faster, resulting in a large variation in the rising slope of the battery voltage (or current) under different charging current conditions. Therefore, it is often not easy to set conditions to accurately detect abnormal charge and discharge behavior of the battery.

[0004] Accordingly, the industry needs a new battery detection method to adapt to various test scenarios of the battery and improve the accuracy of analyzing the battery quality. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a method for detecting the quality of a battery, which can adapt to various test scenarios of the battery and improve the accuracy of analyzing the battery quality.

[0006] The present application provides a method for detecting the quality of a battery, including the following steps: selecting test data with a data concentration greater than a first threshold among a plurality of test data; determining an upper curve and a lower curve of the selected test data; setting a standard detection interval between the upper curve and the lower curve; obtaining a battery characteristic waveform of the battery; and comparing the battery characteristic waveform with the standard detection interval to evaluate the quality of the battery.

[0007] In some embodiments, in the step of determining the upper curve and the lower curve of the selected test data by an image processing program, the following steps may further be included: obtaining an upper contour line and a lower contour line of the selected test data by the image processing program; and generating the upper curve and the lower curve respectively according to the upper contour line and the lower contour line.

[0008] In some embodiments, in the step of determining the upper curve and the lower curve of the selected test data by an image processing program, the following steps may further be included: generating a standard curve according to the selected test data; and generating the upper curve and the lower curve by the image processing program according to the standard curve. Wherein, the image processing program can obtain the upper curve and the lower curve at least by stretching or compressing the standard curve. In addition, in the step of generating the upper curve and the lower curve by the image processing program according to the standard curve, the following steps may further be included: when the change value of the selected test data in a time interval is less than a second threshold, the image processing program translates the standard curve of the time interval to obtain the upper curve and the lower curve of the time interval; and when the change value of the selected test data in the time interval is not less than the second threshold, the image processing program stretches or compresses the standard curve of the time interval to obtain the upper curve and the lower curve of the time interval.

[0009] In some embodiments, the interval bandwidth between the upper curve and the lower curve is the interval bandwidth of the standard detection interval, and the value of the interval bandwidth in a time interval can be at least associated with the standard deviation of the selected test data in the time interval. Herein, the standard curve can be the central value curve or the average value curve of the selected test data. In addition, when the difference between the standard curve and the default curve is greater than a third threshold, the standard curve can be determined to be abnormal.

[0010] In some embodiments, in the step of comparing the battery characteristic waveform with the standard detection interval to evaluate the quality of the battery, the method may further include judging by the image processing program whether the battery characteristic waveform exceeds the standard detection interval to evaluate the quality of the battery.

[0011] The present application provides a battery quality detection method, including the following steps: calculating a value with respect to a standard curve within a first time interval from a plurality of test data; calculating the standard deviation of the plurality of test data within the first time interval; determining the values of an upper limit curve and a lower limit curve within the first time interval based on the standard curve and the standard deviation; setting a standard detection interval, where the standard detection interval is between the values of the upper limit curve and the lower limit curve within the first time interval; and comparing the battery characteristic waveform of the battery within the first time interval with the standard detection interval to evaluate the quality of the battery.

[0012] In some embodiments, the standard curve may be associated with the average or median of the plurality of test data within the first time interval. When the battery characteristic waveform within the first time interval exceeds the standard detection interval, the battery may be determined to be abnormal.

[0013] In summary, different from the traditional method of detecting battery quality which is prone to misjudgment when the battery voltage suddenly changes, and may cause loose control when the battery voltage is stable after increasing the tolerance threshold. The battery quality detection method of the present application dynamically adjusts the standard detection interval with an image processing program. In addition to maintaining strict quality detection when the battery voltage is stable, there is a larger standard detection interval when the battery voltage suddenly changes to reduce the occurrence of misjudgment, thereby improving the accuracy of analyzing the battery quality.

[0014] The other effects and detailed content of the embodiments of the present application will be described below in conjunction with the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 is a schematic diagram for detecting voltage values;

[0017] Figure 2 is another schematic diagram for detecting voltage values;

[0018] Figure 3 is a schematic diagram of a plurality of test data in an embodiment of the present application;

[0019] Figure 4 is a schematic diagram of the plurality of test data after screening in an embodiment of the present application;

[0020] Figure 5 is a schematic diagram of the upper limit curve and the lower limit curve in an embodiment of the present application;

[0021] Figure 6 It is a schematic diagram of the standard curve of an embodiment of the present application;

[0022] Figure 7 It is a schematic diagram of the standard curve, upper limit curve and lower limit curve of an embodiment of the present application;

[0023] Figure 8 It is a flowchart of the steps of a battery quality detection method according to an embodiment of the present application;

[0024] Figure 9 It is a flowchart of the steps of a battery quality detection method according to another embodiment of the present application.

[0025] Symbol description

[0026] 10: Upper limit curve

[0027] 12: Lower limit curve

[0028] 20: Standard curve

[0029] 22: Upper limit curve

[0030] 24: Lower limit curve

[0031] S30 - S38: Process flow

[0032] S40 - S48: Process flow Detailed implementation manners

[0033] In the following implementation manners, the positional relationships described include: up, down, left and right. Unless otherwise specified, they are all based on the directions in which the components are drawn in the drawings.

[0034] For the convenience of explaining the advantages of the battery quality detection method of the present application, please refer to Figure 1 , Figure 1 It is a schematic diagram for detecting voltage values. In addition to the aforementioned that the user can only select and set a fixed upper limit value as the detection standard, the user can also try to give a detection range to improve the correctness of judging anomalies. As Figure 1 shown, in a battery quality detection method, the user can know the charging characteristics and discharging characteristics of the battery according to the battery specifications, such as the battery voltage values under a specific test procedure (formula). Here, the user can plot the corresponding battery voltage values in the specifications as Figure 1 the solid line curve of. In addition, due to battery production errors or detection errors, the user will first translate the Figure 1 solid line curve upward by a certain distance (tolerance threshold d1) and downward by a certain distance (tolerance threshold d2) to form in Figure 1Two dashed lines above and below the solid curve. Generally speaking, as long as the battery is formally measured, as long as the voltage value of the battery is within Figure 1 the detection interval formed by the two dashed lines, it can generally be regarded that there is no abnormality after the battery is detected.

[0035] However, when detecting the charging characteristics of the battery, the charging mode or charging current of the battery will change with the formula to be detected, and the battery voltage value often changes greatly during mode switching. For example, since the battery voltage is much lower than the default voltage value, the charging current is large and the battery voltage rises rapidly, so the waveform slope drawn with the battery voltage value is also large. When the battery approaches the rated voltage and changes to constant voltage charging, the waveform slope drawn with the battery voltage value is the smallest at this stage. The above example shows that the waveform slope is constantly changing. At this time, if only the solid curve is translated (fixed tolerance thresholds d1, d2), there is no problem after time t, because the battery voltage value is relatively stable. If before time t, those of ordinary skill in the art can understand that the battery voltage value is changing rapidly, and the battery voltage value is also easily affected by various measurement errors. However, the tolerance for errors in the detection interval before time t is very low, which easily leads to a considerable number of batteries being judged abnormal at this stage, but actually there may be no problem.

[0036] To solve the above problems, in order to reduce the abnormal misjudgment rate in the stage where the battery voltage value changes rapidly, the user may adjust the tolerance thresholds d1, d2. Please refer to Figure 1 and Figure 2 . Figure 2 is another schematic diagram for detecting voltage values. As shown in the figure, Figure 2 In order to reduce the abnormal misjudgment rate, the detection interval is expanded, for example, the tolerance thresholds d1', d2' are made greater than the original tolerance thresholds d1, d2 respectively. Those of ordinary skill in the art can understand that the widened tolerance thresholds d1', d2' can provide a tolerance for errors before time t, but it brings another problem, that is, the accuracy after time t is reduced, and abnormal battery voltage values may be judged as normal. Accordingly, the present application also proposes the following battery quality detection method.

[0037] The battery quality detection method of the present application is applied to the charge and discharge test of the battery. By measuring the voltage and current of the battery at each time point during the charge and discharge test, and judging the quality of the battery through the measured voltage and current data. Please refer to Figure 3 . Figure 3 is a schematic diagram of multiple test data of an embodiment of the present application. This embodiment demonstrates the voltage data (test data) of many batteries during the charging test, and plots the measured multiple test data as a curve. Figure 3Each curve in it represents the voltage values of the battery at different time points. In this embodiment, a computer is used to generate corresponding curves for each of the foregoing test data, and the computer can overlap the corresponding curves generated from multiple test data in the same display interface, thereby plotting Figure 3 content.

[0038] This embodiment does not limit the generation method of the test data here. For example, the test data may be the test results repeatedly simulated by a program, or obtained by repeatedly actually testing a small number of batteries. In addition, the test data may also be the battery detection results using the same test program (formula) during previous tests, that is, the voltage values of the previous one or more batches of batteries actually recorded. Additionally, the test data may also be obtained by repeatedly testing a standard part (standard battery) in the laboratory.

[0039] Next, since Figure 3 some of the curves in it may deviate too much. For example, when selecting a batch of test data actually obtained previously, the curves of the test data of abnormal batteries are included. To exclude these curves with too large deviations, this embodiment can further screen Figure 3 the curves in it. Refer to Figure 4 , Figure 4 is a schematic diagram of multiple test data after screening in an embodiment of the present application. In practice, this embodiment can screen out the test data with a data concentration greater than the first threshold among multiple test data. In addition, this embodiment can select the threshold of the data concentration according to different test data sources. For example, assuming that as previously mentioned, a batch of test data actually obtained in the past is selected, because it is expected that the test data contains abnormal situations, the test data with a data concentration greater than 30% (the first threshold) can be selected, thereby excluding some test data with large deviations. On the other hand, assuming that this embodiment selects a batch of test data obtained by program simulation or with a standard part, because it is expected that the test data is more accurate, the conditions of the data concentration can be relaxed to utilize more test data.

[0040] After screening the test data, this embodiment can use image processing to obtain the standard detection interval. Please refer to Figure 5 , Figure 5 is a schematic diagram of the upper limit curve and the lower limit curve in an embodiment of the present application. As Figure 5 shown, this embodiment performs image processing on Figure 4 to find Figure 4The upper contour line and the lower contour line of the entire pattern, and the standard detection interval is determined based on the upper contour line and the lower contour line. In practice, the user can directly use the upper contour line and the lower contour line as the upper limit curve 10 and the lower limit curve 12 of the standard detection interval, or can obtain the upper limit curve 10 and the lower limit curve 12 after adjusting the upper contour line and the lower contour line by means of image processing. This embodiment does not impose any restrictions.

[0041] For actual detection, after obtaining the test data of the battery to be tested, the test data can be plotted as a curve (i.e., the battery characteristic waveform), and then compared with the standard detection interval. If the battery characteristic waveform of the battery to be tested crosses the upper limit curve or the lower limit curve (exceeds the standard detection interval), it can be said that the battery to be tested is abnormal. On the contrary, if the battery characteristic waveform of the battery to be tested remains between the upper limit curve and the lower limit curve, it can be said that the battery to be tested is normal. In one example, when detecting whether the battery is abnormal, an image processing program can also be used to automatically compare whether the battery characteristic waveform of the battery to be tested exceeds the standard detection interval. This embodiment does not impose any restrictions. In addition, there may be many other means to evaluate whether the battery to be tested is normal. This embodiment only demonstrates one of the evaluation criteria by judging whether the battery characteristic waveform exceeds the standard detection interval, and other evaluation means will not be elaborated here.

[0042] It is worth mentioning that Figure 4 it can be seen that before the battery is fully charged (time t), due to various factors, the data concentration of the test data varies greatly. After the battery is fully charged (time t), the test data is quite stable. Therefore, Figure 5 the distance (interval bandwidth) between the demonstrated upper limit curve and the lower limit curve is not necessarily the same at each time point. Specifically, Figure 5 it is shown that in the time interval with a large curve slope, the interval bandwidth is significantly larger, while in the time interval with a small curve slope, the interval bandwidth is significantly smaller. This also reflects that the battery quality detection method of this embodiment can actively change the range of the standard detection interval according to different charging modes.

[0043] The above embodiments illustrate the method of obtaining the standard detection interval by means of an image processing program, but this application is not limited thereto. Please refer to Figure 4 、 Figure 6 and Figure 7 , Figure 6 which is a schematic diagram of the standard curve of an embodiment of this application, Figure 7It is a schematic diagram of the standard curve, upper limit curve, and lower limit curve of an embodiment of the present application. As shown in the figure, after screening out the test data with a data concentration greater than the first threshold among multiple test data, the screened multiple test data can be calculated to obtain the standard curve 20. In practice, the standard curve 20 can be associated with the curve (average value curve) drawn based on the average value of the screened multiple test data, or the curve (central value curve) drawn based on the median of the screened multiple test data. As long as the standard curve 20 can be associated with the screened multiple test data, the present embodiment does not limit the means by which the standard curve 20 calculates the screened multiple test data.

[0044] After obtaining the standard curve 20, the upper limit curve 22 and the lower limit curve 24 can also be calculated from the standard curve 20 in this embodiment. In one example, the intervals between the upper limit curve 22 and the standard curve 20, and between the lower limit curve 24 and the standard curve 20 can be calculated. For example, the standard deviation of the screened multiple test data in each time interval can be calculated first. Then, using the standard deviation as a parameter, the upper limit curve 22 and the lower limit curve 24 are drawn based on the standard curve 20. For example, in a time interval, the upper limit curve 22 is the standard curve 20 plus two standard deviations, and the lower limit curve 24 is the standard curve 20 minus two standard deviations. In this way, the upper limit curve 22 and the lower limit curve 24 that reference the standard deviation can also reflect the characteristic that the interval bandwidth is larger in the time interval with a large curve slope, and the interval bandwidth is smaller in the time interval with a small curve slope.

[0045] It is worth mentioning that the upper limit curve 22 and the lower limit curve 24 in a time interval are described above, and the time interval can be distinguished according to the time length or the charging mode. For example, it can be distinguished in units of minutes or hours, or it can be distinguished according to the constant current or constant voltage charging mode, or it can be distinguished according to the magnitude of the charging current (different charging currents correspond to a time interval respectively), and the present embodiment does not limit this. In addition, the acquisition of the standard curve, upper limit curve, and lower limit curve does not require an image processing program. For example, in this embodiment, a computer can directly analyze multiple test data in each time interval, calculate the average value or median of the multiple test data in each time interval, and the standard deviation of the multiple test data in the corresponding time interval. Then, the average value (or median) and the standard deviation are calculated to obtain the values of the upper limit curve and the lower limit curve in the corresponding time interval.

[0046] In one example, assuming that the average value and the standard deviation of a time interval are calculated, then adding one or two standard deviations to the average value gives the value of the upper limit curve of the time interval. Similarly, subtracting one or two standard deviations from the average value of a time interval gives the value of the lower limit curve of the time interval. Those of ordinary skill in the art can understand that after connecting the average values (or medians) of each time interval to each other, the standard curve can be obtained. Moreover, after connecting the values obtained by operating on the average value (or median) of each time interval and the standard deviation to each other, the upper limit curve and the lower limit curve can be obtained. Of course, this embodiment does not limit the operation method of the average value (or median) and the standard deviation, and those of ordinary skill in the art can determine it according to the attributes of the test data or the specifications related to the test.

[0047] In another example, after obtaining the standard curve 20, this embodiment can also determine how to adjust the obtained standard curve 20 by using image processing according to different charging modes to determine the upper limit curve 22 and the lower limit curve 24. For example, because the test data is quite stable (the variation value is less than the second threshold) after the battery is fully charged (time t), the image processing program can generate the upper limit curve 22 and the lower limit curve 24 only by translating the standard curve 20. On the contrary, before the battery is fully charged (time t), the test data fluctuates greatly (the variation value is not less than the second threshold). At this time, the image processing program not only translates the standard curve 20, but also uses the method of stretching or compressing the standard curve 20 to generate the upper limit curve 22 and the lower limit curve 24 to widen the interval bandwidth of this time interval. Finally, the upper limit curve 22 and the lower limit curve 24 of different time intervals processed separately are stitched together to form a complete upper limit curve 22 and a lower limit curve 24. The same as the previous embodiment, the range between the upper limit curve 22 and the lower limit curve 24 is the standard detection interval described in this embodiment. That is to say, this embodiment demonstrates that different charging modes (the degree of battery voltage change) will have corresponding means to determine the standard detection interval, and the standard detection interval can have a corresponding interval bandwidth in each time interval.

[0048] In addition, the standard curve can also be compared with the default curve first to judge the correctness of the standard curve. The default curve can be obtained from the specifications or measured from a standard component, and this embodiment does not limit it. In practice, when the difference between the standard curve and the default curve is too large (greater than the third threshold), it can be judged that the standard curve is abnormal to indicate that the standard detection interval cannot be generated based on this standard curve.

[0049] To illustrate the battery quality detection method of this application, please refer to Figures 3 to 8 , Figure 8It is a flowchart of the steps of a battery quality detection method according to an embodiment of the present application. As shown in the figure, in step S30, test data with a data concentration greater than a first threshold among multiple test data is selected. In step S32, the upper limit curve and the lower limit curve of the selected test data are determined. In step S34, a standard detection interval is set between the upper limit curve and the lower limit curve. In step S36, the battery characteristic waveform of the battery is obtained. And, in step S38, the battery characteristic waveform is compared with the standard detection interval to evaluate the quality of the battery. Since the above steps have been described in the foregoing embodiments and the drawings, they will not be elaborated herein.

[0050] In another example, please also refer to Figures 3 to 9 , Figure 9 It is a flowchart of the steps of a battery quality detection method according to another embodiment of the present application. As shown in the figure, in step S40, a value with respect to a standard curve within a first time interval is calculated from multiple test data. In step S42, the standard deviation of the multiple test data within the first time interval is calculated. In step S44, the value of the upper limit curve and the value of the lower limit curve within the first time interval are determined based on the value of the standard curve and the standard deviation. In step S46, a standard detection interval is set, and the standard detection interval is between the value of the upper limit curve and the value of the lower limit curve within the first time interval. And, in step S48, the battery characteristic waveform of the battery within the first time interval is compared with the standard detection interval to evaluate the quality of the battery. Since the above steps have been described in the foregoing embodiments and the drawings, they will not be elaborated herein.

[0051] Different from the traditional method of detecting battery quality, which is prone to misjudgment when the battery voltage suddenly changes, and widening the tolerance threshold will instead lead to lax control when the battery voltage is stable. The battery quality detection method of the present application dynamically adjusts the standard detection interval with an image processing program. In addition to maintaining strict quality detection when the battery voltage is stable, there is a larger standard detection interval when the battery voltage suddenly changes to reduce the occurrence of misjudgment, thereby improving the accuracy of analyzing the battery quality.

[0052] The above-described embodiments and / or implementation manners are only used to illustrate the preferred embodiments and / or implementation manners for implementing the technology of the present application, and do not impose any formal restrictions on the implementation manners of the technology of the present application. Any person skilled in the art, without departing from the scope of the technical means disclosed in the content of the present application, may make some changes or modifications to other equivalent embodiments, but should still be regarded as the same technology or embodiment substantially the same as the present application.

Claims

1. A battery quality detection method, characterized in that, The method includes: selecting those test data among a plurality of test data whose data concentration is greater than a first threshold; determining an upper limit curve and a lower limit curve of the selected test data; setting a standard detection interval between the upper limit curve and the lower limit curve; obtaining a battery characteristic waveform of a battery; and comparing the battery characteristic waveform and the standard detection interval to evaluate the quality of the battery.

2. The battery quality detection method according to claim 1, characterized in that In the step of determining the upper limit curve and the lower limit curve of the selected test data by an image processing program, it includes: obtaining an upper contour line and a lower contour line of the selected test data by an image processing program; and generating the upper limit curve and the lower limit curve respectively according to the upper contour line and the lower contour line.

3. The battery quality detection method according to claim 1, characterized in that, In the step of determining the upper limit curve and the lower limit curve of the selected test data by an image processing program, it includes: generating a standard curve according to the selected test data; and generating the upper limit curve and the lower limit curve by an image processing program according to the standard curve.

4. The battery quality detection method according to claim 3, characterized in that, The image processing program obtains the upper limit curve and the lower limit curve at least by stretching or compressing the standard curve.

5. The battery quality detection method according to claim 4, wherein In the step of generating the upper limit curve and the lower limit curve by the image processing program according to the standard curve, it includes: when the variation value of the selected test data in a time interval is less than a second threshold, the image processing program translates the standard curve of the time interval to obtain the upper limit curve and the lower limit curve of the time interval; and when the variation value of the selected test data in the time interval is not less than a second threshold, the image processing program stretches or compresses the standard curve of the time interval to obtain the upper limit curve and the lower limit curve of the time interval.

6. The battery quality detection method according to claim 3, characterized in that, The interval between the upper limit curve and the lower limit curve is an interval bandwidth of the standard detection interval, and the value of the interval bandwidth in a time interval is at least related to a standard deviation of the selected test data in the time interval.

7. The battery quality detection method according to claim 6, wherein The standard curve is a central value curve or an average value curve of the selected test data.

8. The battery quality detection method according to claim 3, characterized in that, When the difference between the standard curve and a default curve is greater than a third threshold, it is determined that the standard curve is abnormal.

9. The battery quality detection method according to claim 1, characterized in that, In the step of comparing the battery characteristic waveform and the standard detection interval to evaluate the quality of the battery, it further includes an image processing program judging whether the battery characteristic waveform exceeds the standard detection interval to evaluate the quality of the battery.

10. A battery quality detection method, characterized in that, The method includes: calculating values regarding a standard curve within a first time interval from a plurality of test data; calculating a standard deviation of the test data within a first time interval; determining values of an upper limit curve and a lower limit curve within the first time interval according to the values of the standard curve and the standard deviation; setting a standard detection interval, the standard detection interval being between the values of the upper limit curve and the lower limit curve within the first time interval; and comparing a battery characteristic waveform of a battery within the first time interval and the standard detection interval to evaluate the quality of the battery.

11. The battery quality detection method according to claim 10, wherein The standard curve is related to the average value or median of the test data within the first time interval.

12. The battery quality detection method according to claim 10, wherein When the battery characteristic waveform within the first time interval exceeds the standard detection interval, it is determined that the battery is abnormal.

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