Power battery defect detection method

Abstract

The invention relates to a power battery defect detection method, power battery defect detection equipment and a computer readable storage medium. The method comprises the steps that S1, multiple rounds of cycle testing are conducted on the power battery, each round of cycle testing sequentially comprises a charging section, a standing section, a discharging section and a standing section, and in each round of cycle testing, battery parameters of the power battery in the cycle testing process are measured; s2, analyzing a change curve of the battery parameters of each round of cycle test in multiple rounds of cycle tests to determine a curve statistical parameter value of the change curve; s3, comparing the curve statistical parameter value of each round of cycle test in the multiple rounds of cycle tests with the corresponding ideal curve statistical parameter value, and judging that one or more parameters of the curve statistical parameter values are abnormal in response to the condition that the difference value between the curve statistical parameter value and the ideal curve statistical parameter value exceeds a preset difference value threshold value; and S4, judging one or more defects of the power battery according to the parameter abnormity.

Description

Technical Field

[0001] The present disclosure relates to the field of batteries, and in particular, to a power battery defect detection method and a power battery defect detection device, and a computer-readable storage medium. Background Art

[0002] During the production, assembly and use of power batteries, various defects may occur inside the battery cells. For example, ultrasonic welding may be used in the manufacturing process of the battery cells, which may cause cold welding in some parts. During the module assembly process, the tabs may easily break, etc. These defects affect the performance of the battery cells and further affect the performance and safety of the power batteries. However, conventional inspection methods for power battery production lines are difficult to identify defects inside power batteries, and the disassembly inspection method may have problems such as complex disassembly work, high cost, slow inspection results, and battery cell damage. Summary of the invention

[0003] In view of the above problems, the present disclosure aims to provide a power battery defect detection method and a power battery defect detection device, and a computer-readable storage medium.

[0004] According to a first aspect of the present disclosure, a power battery defect detection method is provided, comprising the following steps: S1: performing multiple rounds of cycle tests on the power battery, wherein each round of cycle tests sequentially includes a charging section, a rest section, a discharging section and a rest section, and wherein, in each round of cycle tests, battery parameters of the power battery during the cycle tests are measured; S2: analyzing a change curve of the battery parameters of each cycle test in the multiple rounds of cycle tests to determine one or more curve statistical parameter values ​​of the change curve; S3: comparing one or more curve statistical parameter values ​​of each cycle test in the multiple rounds of cycle tests with corresponding ideal curve statistical parameter values, and determining that there are one or more parameter abnormalities in the curve statistical parameter values ​​in response to a difference between one or more curve statistical parameter values ​​and the ideal curve statistical parameter values ​​exceeding a preset difference threshold; and S4: determining one or more defects of the power battery based on the one or more parameter abnormalities.

[0005] According to the power battery defect detection method of the embodiment of the present disclosure, step S4 also includes: in response to the fact that the curve statistical parameter values ​​of the cycle tests exceeding a preset number of rounds in multiple cycle tests all have the same parameter abnormality, determining that the power battery has a defect corresponding to the parameter abnormality.

[0006] According to the power battery defect detection method of the embodiment of the present disclosure, in step S1, the charging rate of the charging segment of each round of cycle test of multiple rounds of cycle tests is determined according to the cut-off voltage.

[0007] According to the power battery defect detection method of an embodiment of the present disclosure, the battery parameters include any one or more of the following: temperature, voltage, current, battery capacity, DC internal resistance and AC internal resistance.

[0008] According to the power battery defect detection method of the embodiment of the present disclosure, the multi-round cycle test includes at least 3 rounds.

[0009] According to the power battery defect detection method of the embodiment of the present disclosure, wherein the battery parameters include voltage, the method further includes: in response to the voltage in the discharge sub-segment below 20% of the remaining power in the discharge segment in each round of cycle testing being higher than a preset voltage threshold, determining that the battery has a poor welding defect.

[0010] According to the power battery defect detection method of the embodiment of the present disclosure, the curve statistical parameter value includes one or more of the following: curve slope, battery parameter average value, standard deviation and variance of battery parameter measurement value and corresponding ideal parameter value.

[0011] According to the power battery defect detection method of the embodiment of the present disclosure, the battery form includes one or more of the following: soft-pack battery, square shell battery, cylindrical battery, the battery positive electrode material includes one or more of the following: nickel cobalt manganese oxide positive electrode material, nickel cobalt aluminum oxide lithium electrode material, lithium manganese oxide positive electrode material, lithium cobalt oxide positive electrode material, lithium iron phosphate positive electrode material, the battery negative electrode material includes one or more of the following: graphite negative electrode material, silicon oxide negative electrode material, silicon-carbon composite negative electrode material, lithium titanate negative electrode material, lithium metal negative electrode material, nano negative electrode material.

[0012] According to a second aspect of the present disclosure, a power battery defect detection device is provided, which includes a memory and a processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the method described in any one of the aforementioned embodiments is implemented.

[0013] According to a third aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0014] The present invention analyzes the parameter changes of the charge and discharge curves of the power battery under the cycle test, determines the abnormal conditions of the parameter curve and corresponds them to the corresponding internal defects of the battery. It can quickly and accurately reflect the defects inside the battery without disassembling the battery, and has high detection convenience and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other objects and advantages of the present disclosure will become more fully apparent from the following detailed description taken in conjunction with the accompanying drawings, wherein the same or similar elements are denoted by the same reference numerals.

[0016] Figure 1 A schematic flow chart of a power battery defect detection method 100 according to some embodiments is shown. DETAILED DESCRIPTION

[0017] The description of the following specific embodiments is merely exemplary in nature and is not intended to limit the disclosed technology or the application and use of the disclosed technology. In addition, it is not intended to be bound by any express or implied theory presented in the aforementioned technical field, background technology or the following specific embodiments.

[0018] In the following detailed description of the embodiments, many specific details are set forth in order to provide a more thorough understanding of the disclosed technology. However, it is apparent to one of ordinary skill in the art that the disclosed technology can be practiced without these specific details. In other instances, well-known features are not described in detail to avoid unnecessarily complicating the description.

[0019] Terms such as “comprise” and “include” indicate that in addition to the units and steps directly and explicitly stated in the specification, the technical solution of the present disclosure does not exclude the situation where it has other units and steps that are not directly or explicitly stated.

[0020] Hereinafter, various exemplary embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings.

[0021] Figure 1 A schematic flow chart of a power battery defect detection method 100 according to some embodiments is shown. The power battery defect detection method 100 disclosed in the present invention may include the following steps:

[0022] In step S1, the power battery is subjected to multiple rounds of cycle tests, wherein each round of cycle tests includes a charging section, a rest section, a discharge section and a rest section in sequence, and wherein, in each round of cycle tests, the battery parameters of the power battery during the cycle tests are measured. Performing multiple rounds of cycle tests on the power battery is conducive to discovering measurement parameter anomalies that are not easy to occur in a single round of testing. For example, at least 3 rounds of cycle tests can be performed on the power battery, and each round of cycle tests includes a process of charging-resting-discharging-resting. In some examples, charging and discharging can be full charging and discharging to detect the full change of the measurement parameter curve of the power battery during the whole process of charging and discharging. However, it can also be a process for a certain section of charging or discharging to locate battery defects for specific curve anomalies, for example, for the end of charging or discharging. The rest section can be conducive to the transition of the charging and discharging process, for example, a rest section of 0.5 hours, 1 hour, 2 hours, etc. can be set.

[0023] In step S2, the change curve of the battery parameters in each cycle test in the multiple cycle tests is analyzed to determine one or more curve statistical parameter values ​​of the change curve. After measuring the battery, a change curve of the battery-related parameters can be obtained, for example, a curve of the voltage change over time. It can be understood that the aforementioned change curve can be a discharge curve and / or a charging curve. For the change curve, one or more curve statistical parameter values ​​can be counted, and the curve statistical parameter value reflects the statistical value of a certain battery parameter during the detection process, such as the mean value, slope, derivative, variance, standard deviation, etc.

[0024] In step S3, one or more curve statistical parameter values ​​of each round of cycle tests in multiple rounds of cycle tests are compared with the corresponding ideal curve statistical parameter values, and in response to the difference between one or more curve statistical parameter values ​​and the ideal curve statistical parameter values ​​exceeding the preset difference threshold, it is determined that one or more parameter abnormalities exist in the curve statistical parameter values. In each round of cycle tests in multiple rounds of cycle tests, the measured and calculated curve statistical parameter values ​​are compared with the ideal values ​​corresponding to the curve statistical parameter values ​​to determine whether they exceed the preset comparison (difference) threshold, thereby determining whether the curve statistical parameter value is abnormal. For example, for the curve statistical parameter value of "curve slope" in the voltage versus time curve, an ideal curve slope value x1 and a difference threshold Δx can be preset, and the difference between the measured slope values ​​x2 and x1 at each moment or time period in multiple rounds of tests is compared with the difference threshold Δx. If the difference threshold Δx is exceeded, it is determined that the curve statistical parameter value is abnormal.

[0025] In step S4, one or more defects of the power battery are determined based on one or more abnormal parameters. According to whether there are abnormalities in one or more curve statistical parameter values ​​obtained by analysis and comparison, the defect type and degree of the battery can be determined according to the abnormal type of the curve statistical parameter value and the battery parameter type, or the defect type (for example, cold welding, internal structural defects of the battery) and the degree of defects of the battery can be estimated. These abnormalities can be, for example, abnormal lower limit of the discharge cut-off voltage of the battery cell (the voltage difference in the abnormal range can include, for example, 0V to 1V), battery cell voltage jump (for example, including charging section voltage jump, static section voltage jump, discharge section voltage jump). The battery / battery cell defects that can be detected include, for example, welding defects inside the battery / battery cell (for example, cold welding, welding breakage, deformation, burrs, etc. caused by ultrasonic welding), or defects in the internal structure of the battery / battery cell (including such as positive electrode sheet tearing, positive electrode sheet folding, negative electrode sheet tearing, negative electrode sheet folding, diaphragm folding, lithium precipitation, etc.).

[0026] For example, for the abnormal slope of the above-mentioned curve, for example, the torn ear can be determined or estimated. Specifically, the corresponding voltage change curves can be analyzed separately. When checking the slope of the discharge curve, if a sudden jump is found at the end of the curve when it is close to full discharge, the voltage surge ranges from a few millivolts to more than ten millivolts (the maximum can reach 51mV), and this phenomenon may occur repeatedly during repeated charge and discharge. The cause of the sudden increase can be analyzed. For example, due to poor welding of the laminated battery cell, the ear contact of some of the pole pieces inside the battery cell is poor. During the process of charge and discharge expansion changes, the phenomenon of overlap-disconnection-lap occurs, causing the battery cell capacity to fluctuate, which in turn causes abnormal charge and discharge curves.

[0027] Therefore, after analyzing the curve anomaly and defect type, etc., an alarm and sound notification can be issued in an appropriate manner, or it can be displayed in a visual form on the interactive interface of the detection equipment.

[0028] Method 100 realizes rapid detection of power batteries, and the detection process can be automated, automatically performing cyclic testing and measurement value analysis according to the process of the method, and using multiple rounds of detection to reduce detection / test errors and save detection costs. Moreover, there is no need to disassemble the battery / cell, achieving non-destructive and efficient detection of the battery / cell.

[0029] In some embodiments, step S4 may further include: in response to the same parameter abnormality in the curve statistical parameter values ​​of the cycle tests exceeding the preset number of rounds in the multiple cycle tests, it is determined that the power battery has a defect corresponding to the parameter abnormality. That is to say, in some examples, there may be a situation where the curve statistical parameter value abnormality reflected by a single cycle test is not sufficient to determine the defect type of the corresponding battery, so the requirement for the number of cycle tests to measure the abnormality of the curve statistical parameter value can be increased. For example, for the curve statistical parameter value of "curve slope" in the voltage-time curve, 5 rounds of cycle tests are performed. If there is only one round of cycle test with abnormal curve slope, it is not determined that there is a corresponding battery defect. If there are 2 or more rounds of curve slope abnormalities, it can be determined that there is a corresponding battery defect. In this way, while multiple rounds of tests can further ensure that the parameter abnormality caused by battery defects can be captured, some occasional factors that are not obvious battery defects can be excluded, thereby further improving the accuracy of detection. In some examples, multiple rounds of cycle tests include at least 3 rounds of tests, which can have a higher test tolerance for most or all types of battery defects and improve the accuracy of battery defect determination.

[0030] In some embodiments, the charging rate of the charging segment of each round of the multiple cycle test is determined according to the cut-off voltage. For example, the charging of the charging segment of each round of the multiple cycle test in the above step S1 is implemented according to the decreasing charging rate of different cut-off voltages. For example, the battery cell can be charged with different currents according to different cut-off voltages. In some examples, a step-down charging strategy is used. For example, for the charging of the ternary lithium battery test, when the charging is not yet at the cut-off voltage of 3.8V, a charging rate of 3C is adopted, when the cut-off voltage of 3.8V is reached but not to 3.9V, a charging rate of 1.5C is adopted, and when the cut-off voltage of 3.9V is reached but not to 4V or less, a charging rate of 0.5C is adopted. In this way, charging can be performed at a faster rate, saving test time, and the step-down charging rate also makes the battery charge orderly decelerate, avoiding the adverse effects of rapid rate changes. In addition, the use of rate testing can also amplify the internal defects of the battery / battery cell, which is conducive to detecting the internal defects.

[0031] The test current for the battery / cell applicable to this method may be, for example, in the range of 0A to 1000A. Therefore, the current size can be set according to the capacity of the battery / cell, and the corresponding test rate can also be set according to different cut-off voltages during the above-mentioned charging process test, such as 3C, 1C, 1 / 3C, 1 / 20C, etc. Alternatively, in other examples, the discharge rate can be decreased according to the remaining capacity of the battery, for example, 1C discharge is used when the remaining capacity is greater than 50%; 0.5C discharge is used when the remaining capacity is between 20% and 50%; and 0.2C discharge is used when the remaining capacity is less than 20%.

[0032] In some embodiments, the above-mentioned battery parameters may include any one or more of the following: temperature, voltage, current, battery capacity, DC internal resistance and AC internal resistance. That is, one or more of these parameters may be measured simultaneously in multiple rounds of cycle testing, and the specific measured parameter items may be determined according to the battery defects to be detected, for example, the internal tab tearing defect of the battery to be detected may be reflected by the change in the slope of the voltage curve during the charge and discharge voltage process. Therefore, it can be understood that the curve statistical parameter value may include any statistical value, for example, it may be one or more of the following: curve slope, battery parameter average value, standard deviation of the battery parameter measurement value and the corresponding ideal parameter value, variance, first derivative, second derivative obtained from the curve or measurement value, etc. The curve statistical parameter value may also be the value of the battery parameter itself, the change in the value of the battery parameter in a certain period of time (such as voltage drop) or the time used for charging and discharging, so it may correspond to abnormal parameter value size, abnormal change amount and abnormal time. It should be understood that in this method, the analysis method of the test parameters includes but is not limited to correlation analysis, correspondence analysis, regression analysis, variance analysis, etc.

[0033] In this article, the power battery can be, for example, a battery cell group including multiple battery cells, or a single battery cell, module, or battery pack. Among them, the form of the battery can be, for example, a soft pack battery, a square shell battery, a cylindrical battery, etc., the battery positive electrode material can, for example, include nickel cobalt manganese oxide (ternary), nickel cobalt aluminum oxide (ternary), lithium manganese oxide, lithium cobalt oxide, lithium iron phosphate, and modified materials doped with transition metal elements, etc., the battery negative electrode material can, for example, include graphite, silicon oxide, silicon-carbon composite negative electrode material, alloy negative electrode material, lithium titanate, lithium metal negative electrode material, nano negative electrode material, etc., and the electrolyte can, for example, include liquid electrolyte, solid-liquid mixed electrolyte, and all-solid electrolyte. Therefore, this method can differentiate battery parameter measurement and statistical value analysis for different power battery types, materials, etc.

[0034] Preferably, the power battery can be tested at, for example, -40° C. to 60° C. However, the test can also be performed at room temperature, and there is no need to set up a high or low temperature environment.

[0035] In some embodiments, the battery parameter may be voltage, in which case the method 100 may further include: in response to the voltage in the discharge sub-segment below 20% of the remaining power in the discharge segment in each round of cycle testing being higher than a preset voltage threshold, determining that the battery has a poor welding defect. That is, first, a rate fast charge cycle test is performed on the battery cell with potential defects (for example, the battery cell is charged with different currents according to different cut-off voltages as described above. Specifically, when the voltage is lower than 3.8V, a charging rate of 3C is used, between 3.8V and 3.9V, a charging rate of 1.5C is used, between 3.9V and 4.0V, a charging rate of 1C is used, and when the voltage reaches 4.0V to 4.2V, a charging rate of 0.5C is used). Then, after charging is completed, the battery cell needs to be left to stand for two hours, then discharged at a standard rate of 0.5C, and left to stand for two hours again. This charge and discharge cycle process is repeated at least three times to ensure the reliability and repeatability of the data. By analyzing the voltage change curve during the charge and discharge process, especially checking the slope of the discharge curve when it is close to the full discharge state, it can be observed that at the end of the curve (for example, when the last 20% of the discharge section is discharged), the voltage has an obvious jump phenomenon (for example, greater than the preset threshold or significantly greater than (for example, 3-5 times or more) the preset threshold), and the voltage jump ranges from a few millivolts to more than ten millivolts, up to 51mV. This phenomenon can be reproduced in multiple rounds of charge and discharge cycles. It can be determined that this voltage jump is caused by poor welding of the internal stacking sheets of the battery cell. Specifically, this can lead to poor contact of the pole ears of some pole sheets, and the phenomenon of overlap-disconnection-lap occurs during the charge and discharge process due to the expansion and contraction of the battery cell, thereby causing fluctuations in the battery cell capacity and ultimately leading to abnormal charge and discharge curves.

[0036] In addition, the presence of welding defects can also be determined based on other measurement parameters, such as the change in the DCR (direct current internal resistance) value showing a gradual increase trend in multiple rounds of cycle testing (for example, the change in each round is greater than 2%). For lithium deposition defects (phenomenon), the ACR (alternating current internal resistance) and DCR can be detected to be significantly increased (such as more than 10% of the preset normal value), and the discharge curve has nonlinear fluctuations.

[0037] In some examples, the power batteries involved in the present disclosure are in various forms, including but not limited to soft-pack batteries, square-shell batteries and cylindrical batteries. Soft-pack batteries are widely used in electric vehicles and portable electronic devices due to their lightweight design and flexible shape adaptability; square-shell batteries have high energy density and good mechanical stability, and are suitable for large energy storage systems and electric vehicles; cylindrical batteries are common in power tools and small electric vehicles due to their standardized size and mature production process. The selection of battery positive electrode materials is also very rich, including but not limited to nickel cobalt manganese oxide (ternary) positive electrode materials, which have high energy density and good cycle performance; nickel cobalt aluminum oxide (ternary) positive electrode materials, which are suitable for high power output occasions; lithium manganese oxide positive electrode materials, which have low cost and good safety; lithium cobalt oxide positive electrode materials, which are mainly used in consumer electronics products and have high energy density; lithium iron phosphate positive electrode materials, which have excellent safety and long life characteristics; in addition, modified materials doped with transition metal elements can also be used to further optimize electrochemical performance. Battery negative electrode materials are also diverse, including but not limited to graphite negative electrode materials, which have good stability and low cost; silicon oxide negative electrode materials, which provide higher specific capacity; silicon-carbon composite negative electrode materials, which combine the high capacity of silicon and the stable performance of carbon; lithium titanate negative electrode materials, which have excellent cycle stability and fast charge and discharge capabilities; lithium metal negative electrode materials, which have extremely high energy density; nano-negative electrode materials, which use nanotechnology to increase the surface area and reaction activity of materials, thereby improving the overall performance of the battery. The disclosed solution can effectively identify and detect various defects inside the battery cell, such as poor welding, pole piece tearing, diaphragm folding and lithium precipitation, for different battery forms and material combinations, thereby ensuring the stability and safety of the battery and improving the reliability and service life of the overall system.

[0038] In other embodiments, different weights can be set for different battery parameters in the power battery defect detection method. To set a multi-parameter weight model, it is necessary to first select key parameters, such as voltage jump, internal resistance change rate, capacity decay rate, etc., and combine historical data and experimental results to calculate the correlation between each parameter and a specific defect, and determine the weight based on the correlation coefficient or feature importance score. For example, for poor welding, the voltage jump battery parameter weight can be set to 0.4, the internal resistance change rate battery parameter weight can be set to 0.35, and the capacity decay rate battery parameter weight can be set to 0.25. In actual testing, the multi-parameter data of each round of cycle testing is collected, the deviation value of each parameter is multiplied by the weight, and the abnormal score is accumulated to generate an abnormal score. Compared with the preset threshold (such as score>0.7), the defect can be judged. Assuming that the voltage jump deviation of a certain test is 0.15, the internal resistance deviation is 0.1, and the capacity decay deviation is 0.05, the comprehensive score is 0.4×0.15+0.35×0.1+0.25×0.05=0.1075, which is lower than the threshold, it is judged that there is no defect. In this way, high sensitivity and applicability to different battery parameters can be combined to suit different battery types and test conditions.

[0039] According to another aspect of the present disclosure, a power battery defect detection device is provided, which includes a memory and a processor. The memory stores instructions, and when the instructions are executed by the processor, any one of the battery defect detection methods 100 based on the charging process described above is implemented.

[0040] According to another aspect of the present disclosure, a computer-readable storage medium is provided, in which instructions are stored. When the instructions are executed by a processor, the processor executes any one of the power battery defect detection methods 100 described above. The computer-readable medium referred to in the present disclosure includes various types of computer storage media, which can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the computer-readable medium can include RAM, ROM, EPROM, EEPROM, etc. 2PROM, register, hard disk, removable disk, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or can be used to carry or store the desired program code unit with instruction or data structure form and can be accessed by general or special computer, or any other temporary or non-temporary medium of general or special processor. As used herein, the disk usually copies data magnetically, while the dish uses laser to optically copy data. The above combination should also be included in the protection scope of computer-readable medium. Exemplary storage medium is coupled to the processor so that the processor can read and write information from / to the storage medium. In an alternative solution, the storage medium can be integrated into the processor. The processor and the storage medium can reside in the ASIC. The ASIC can reside in the user terminal. In an alternative solution, the processor and the storage medium can reside in the user terminal (e.g., test, diagnosis, detection equipment) as discrete components.

[0041] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Those skilled in the art can think of other feasible changes or substitutions based on the technical scope disclosed in the present disclosure, and such changes or substitutions are all included in the protection scope of the present disclosure. In the absence of conflict, the embodiments of the present disclosure and the features in the embodiments can also be combined with each other. The protection scope of the present disclosure shall be based on the records of the claims.

Claims

1. A power battery defect detection method, characterized in that: The method comprises the following steps: S1: performing multiple rounds of cycle tests on the power battery, wherein each round of cycle tests sequentially includes a charging section, a rest section, a discharging section and a rest section, and wherein, in each round of cycle tests, battery parameters of the power battery during the cycle tests are measured; S2: analyzing a variation curve of the battery parameter in each cycle test in the multiple cycle tests to determine one or more curve statistical parameter values ​​of the variation curve; S3: comparing the one or more curve statistical parameter values ​​of each round of the multiple rounds of test with the corresponding ideal curve statistical parameter values, and determining that one or more parameter abnormalities exist in the curve statistical parameter values ​​in response to a difference between the one or more curve statistical parameter values ​​and the ideal curve statistical parameter values ​​exceeding a preset difference threshold; and S4: Determine one or more defects of the power battery based on the one or more abnormal parameters.

2. According to the method of claim 1, the step S4 further comprises: In response to the fact that the statistical parameter values ​​of the curves of the cycle tests exceeding a preset number of rounds in the multiple cycle tests all have the same parameter abnormality, it is determined that the power battery has a defect corresponding to the parameter abnormality.

3. The method according to claim 1, characterized in that In step S1, the charging rate of the charging segment of each round of the multiple rounds of cycle tests is determined according to the cut-off voltage.

4. The method according to claim 1, characterized in that The battery parameters include any one or more of the following: temperature, voltage, current, battery capacity, DC internal resistance and AC internal resistance.

5. The method according to claim 1, characterized in that: The multi-round cycle test includes at least 3 rounds.

6. The method according to claim 1, characterized in that The battery parameter includes voltage, and the method further includes: in response to the voltage in the discharge sub-segment below 20% of the remaining power in each cycle test being higher than a preset voltage threshold, determining that the battery has a poor welding defect.

7. The method according to claim 1, characterized in that The curve statistical parameter value includes one or more of the following: curve slope, battery parameter average value, standard deviation and variance of battery parameter measurement value and corresponding ideal parameter value.

8. The method according to claim 1, characterized in that: The battery form includes one or more of the following: soft-pack battery, square shell battery, cylindrical battery, the battery positive electrode material includes one or more of the following: nickel cobalt manganese oxide positive electrode material, nickel cobalt aluminum oxide positive electrode material, lithium manganese oxide positive electrode material, lithium cobalt oxide positive electrode material, lithium iron phosphate positive electrode material, the battery negative electrode material includes one or more of the following: graphite negative electrode material, silicon oxide negative electrode material, silicon-carbon composite negative electrode material, lithium titanate negative electrode material, lithium metal negative electrode material, nano negative electrode material.

9. A power battery defect detection device, characterized in that: The power battery defect detection device includes a memory and a processor, wherein the memory stores instructions, and when the instructions are executed by the processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, which, when executed by a processor, implement the method according to any one of claims 1-8.

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