A method, device and computer equipment for optimizing a core short circuit test parameter

Abstract

The present application belongs to the technical field of lithium ion battery detection, and particularly relates to a winding core short circuit test parameter optimization method, device and computer equipment. The winding core short circuit test parameter optimization method comprises the following steps: constructing a defect winding core sample library; taking voltage, plane pressure and test time as cooperative optimization parameters, performing full-factor experiments on the defect winding core sample library and normal control samples under different parameter combinations, obtaining a defect detection rate data set and a normal winding core damage rate data set; taking a defect detection rate not lower than a first threshold value and a normal winding core damage rate not higher than a second threshold value as an optimization target, determining an optimal parameter combination; and introducing the optimal parameter combination into a production line for trial operation verification, and determining final target test parameters according to a verification result. The present application solves the technical problems in the prior art, such as test parameter setting depending on experience, lacking systematic verification, not considering the key role of pressure parameters and being unable to balance high detection rate and zero damage rate.

Description

Technical Field

[0001] This invention belongs to the field of lithium-ion battery testing technology, specifically relating to a method, apparatus, and computer equipment for optimizing short-circuit test parameters of battery cores. Background Technology

[0002] Lithium-ion batteries, due to their high energy density and long cycle life, have become the core power source for new energy vehicles, energy storage systems, and consumer electronics. The core, as a key component for energy storage and conversion within the battery, directly determines the battery's final safety, reliability, and lifespan through its manufacturing quality, especially the integrity of its internal insulation. In the complex production processes of core winding, stacking, hot pressing, and assembly, various potential defects are easily introduced due to limitations in process precision, environmental cleanliness, equipment condition, and raw material quality. These defects mainly fall into two categories: physical defects, such as membrane wrinkles or folds caused by uneven winding tension or mechanical stress, and foreign metal objects (such as copper, iron, and zinc particles) introduced by equipment wear, environmental dust, or raw material entrainment; and process defects, such as electrode burrs and micro-perforations in the separator caused by poor cutting. The aforementioned defects can all create micro-short circuits or short-circuit channels inside the battery. In mild cases, this can lead to increased self-discharge and accelerated capacity decay. In severe cases, it can cause localized thermal runaway during use or charging, resulting in catastrophic safety accidents such as battery fires and explosions.

[0003] Therefore, efficient and accurate short-circuit defect detection of the core before battery encapsulation and electrolyte filling, and timely rejection of defective products, is an indispensable key step in ensuring the quality of battery products leaving the factory and the safety of end-use. High-voltage withstand voltage (Hi-pot) testing is currently the mainstream and effective technical means in the industry for detecting insulation defects inside the core. Its basic principle is to apply a DC or pulsed test voltage much higher than its normal operating voltage between the positive and negative terminals of the core. If the aforementioned defects exist inside the core, partial discharge, dielectric breakdown, or direct conduction will occur at the defect point (such as the tip of a metal foreign object, the contact point of a separator fold, or a pinhole) due to the sharp concentration of electric field intensity. This manifests as a sudden drop in insulation resistance or an abnormal increase in leakage current. By monitoring the changes in these electrical parameters, cores with short-circuit risks can be identified.

[0004] Although the Hi-pot test principle is clear, it still has significant shortcomings in practical applications for large-scale industrial production:

[0005] On the one hand, the testing parameters lack a scientific and systematic verification system, relying heavily on experience-based settings. This "experience-driven" approach leads to blind and arbitrary parameter settings. If the parameters are set too aggressively (voltage or pressure too high), it can easily cause an undefective diaphragm to be accidentally punctured, resulting in "over-detection" (misjudging a good product as a defective one). Conversely, if the parameters are set too conservatively (voltage or pressure too low), a sufficiently strong electric field or mechanical contact cannot be formed at the defect point, resulting in "missed detection."

[0006] On the other hand, parameter optimization is too simplistic and fails to consider the synergistic effects of multiple parameters. For example, the method disclosed in reference document CN202510019276A involves testing the breakdown voltage of the diaphragm under different conditions, such as raw materials, core, and after baking, and setting the test voltage as a certain percentage of these values. This method ignores the fact that actual short circuits are the result of the coupling effect between defects and electrical and mechanical fields.

[0007] In summary, the core challenge facing existing Hi-pot testing technology is how to determine an optimal combination of test parameters through a scientific, systematic, and quantifiable method. This would ensure near 100% detection of all types of potential short-circuit defects while completely avoiding any damage to normal battery cores. This is a fundamental requirement for achieving the dual goals of "precise detection" and "non-destructive protection" in large-scale battery production, and it is also a key technological hurdle that urgently needs to be overcome. Summary of the Invention

[0008] To address the aforementioned problems, the present invention aims to provide a method, apparatus, and computer device for optimizing core short-circuit test parameters, thereby resolving the technical issues in the prior art where Hi-pot test parameter setting relies on experience, lacks systematic verification, fails to consider the key role of pressure parameters, and cannot simultaneously achieve high detection rates and zero damage rates.

[0009] The specific technical solution of the present invention is as follows:

[0010] A method for optimizing short-circuit test parameters of a winding core includes the following steps:

[0011] S1. Construct a defective core sample library;

[0012] S2. Using voltage, plane pressure and test time as co-optimization parameters, conduct full factorial experiments on the defective core sample library and normal control samples under different parameter combinations to obtain defect detection rate dataset and normal core damage rate dataset.

[0013] S3. Based on the defect detection rate dataset and the normal core damage rate dataset, with the defect detection rate not lower than the first threshold and the normal core damage rate not higher than the second threshold as the optimization objective, the optimal parameter combination is determined through data quantitative analysis.

[0014] S4. Import the optimal parameter combination into the production line for trial operation and verification, obtain the verification results, and determine the final target test parameters based on the verification results.

[0015] As a further preferred embodiment of the present invention, the first threshold is 99% and the second threshold is 0%.

[0016] As a further preferred embodiment of the present invention, the defective core sample library includes diaphragm wrinkle defect samples and metal foreign object defect samples.

[0017] As a further preferred embodiment of the present invention, the full factorial experiment under different parameter combinations in step S2 includes:

[0018] Determine the plane pressure and test time, adjust the voltage within a preset range, and record the detection of defects and the damage of normal samples at each voltage point;

[0019] Determine the voltage and test time, adjust the plane pressure within a preset range, and record the detection of defects and the damage of normal samples at each plane pressure point;

[0020] Determine the plane pressure and voltage, adjust the test time within a preset range, and record the detection of defects and the damage to normal samples at each test time point.

[0021] As a further preferred embodiment of the present invention, the preset range of the voltage is ±50% of the mass production experience voltage value; the preset range of the planar pressure is the actual force range of the core assembly.

[0022] As a further preferred embodiment of the present invention, the preset range of the test time is 50ms to 400ms.

[0023] As a further preferred embodiment of the present invention, the optimal parameter combination includes: voltage ≥ 100V, plane pressure ≥ 3.04N / mm. 2 Test time ≥ 300ms.

[0024] As a further preferred embodiment of the present invention, it further includes a step of determining a judgment threshold K based on the optimal parameter combination, wherein the formula for calculating the value of K is:

[0025] ;

[0026] Where △V is the attenuation voltage difference of the test voltage pulse, △T is the time interval between two open-circuit voltage tests; 0CV2 is the second battery open-circuit voltage, 0CV3 is the third battery open-circuit voltage, T2 is the test time for the second open-circuit voltage, and T3 is the test time for the third open-circuit voltage.

[0027] A device for optimizing short-circuit test parameters of a winding core includes:

[0028] The sample library component module is used to build a sample library of defective cores;

[0029] The collaborative experiment module is used to perform full factorial experiments on the defective core sample library and normal control samples under different parameter combinations, using voltage, plane pressure and test time as collaborative optimization parameters, to obtain defect detection rate dataset and normal core damage rate dataset.

[0030] The parameter optimization module, based on the defect detection rate dataset and the normal core damage rate dataset, takes the defect detection rate not lower than the first threshold and the normal core damage rate not higher than the second threshold as the optimization objective, and determines the optimal parameter combination through data quantitative analysis.

[0031] The production verification module imports the optimal parameter combination into the production line for trial operation verification, obtains the verification results, and determines the final target test parameters based on the verification results.

[0032] A computer device includes a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of any of the methods described above.

[0033] The beneficial effects of this invention are as follows:

[0034] This invention establishes a scientific and closed-loop parameter verification system, completely transforming Hi-pot parameter setting from "experience-driven" to "data-driven," providing a solid and traceable scientific basis for parameter decision-making.

[0035] This invention breaks through the limitations of traditional methods that only focus on voltage or time, reveals the key role of pressure in the detection of structural defects, and finds the optimal matching point among voltage, pressure and time through multi-parameter collaborative experiments, solving the problem that single-parameter optimization cannot take into account various types of defects.

[0036] Guided by the quantitative goals of a detection rate of ≥99% and a second threshold damage rate of 0%, this invention ensures that the optimized parameter combination can both intercept safety hazards to the maximum extent and absolutely protect the production of good products, directly responding to the core demands of large-scale production.

[0037] This invention is optimized based on a standardized defect sample library covering multiple types and gradients, so that the final parameters have a wide detection capability for various short-circuit risks that may occur in production.

[0038] The optimization results of this invention are not only theoretical values, but also standardized test parameters that have been verified through actual production trials and can be directly imported to guide mass production. This greatly shortens the process debugging cycle and improves production quality and efficiency. Attached Figure Description

[0039] Fig. 1 The detection rate trends of various short-circuited batteries under different voltages are shown.

[0040] Fig. 2 The trend of detection rate under different surface pressures for various types of short-circuited batteries.

[0041] Fig. 3 The detection rate trends of various short-circuit batteries under different test times are shown.

[0042] Fig. 4 Perform statistical analysis on the K value.

[0043] Fig. 5 A structural block diagram of the device for implementing the method of optimizing the short-circuit test parameters of the core winding.

[0044] Fig. 6 A structural block diagram of a computer device for implementing a method to optimize short-circuit test parameters for core windings. Detailed Implementation

[0045] In the description of this invention, it should be understood that the terms "upper," "lower," "inner," "outer," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0046] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, terms such as "set" and "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0047] A core concept of this invention lies in abandoning the single, isolated approach to parameter adjustment for Hi-pot testing of square aluminum shell coils, and instead adopting a systematic and engineered collaborative optimization and verification method. This method is based on defect samples closely resembling actual production conditions, using voltage, pressure, and time as three equal optimization dimensions. Through rigorous full-factor experiments, it acquires massive amounts of performance data and ultimately, driven by the extreme dual objectives of "zero defective product misses and zero defective product damage," selects a universally applicable, reliable, and directly mass-production-ready optimal test parameter package.

[0048] The traditional Hi-pot testing paradigm assumes that electrical breakdown occurs through electric field, assuming that defect detection relies primarily on the strong electric field generated by high voltage to induce electrical stress at the defect point (such as the tip of a metal foreign object), thus breaking down the diaphragm. Under this framework, pressure is merely an auxiliary condition to ensure electrical contact, and its quantitative importance has not been quantified.

[0049] The inventors discovered that for structural defects (represented by diaphragm wrinkles / folds), the failure mechanism is closer to "electromechanical coupling conduction." Pressure here is not an auxiliary condition, but rather a key switch determining whether the defect can be "activated" and exposed as a detectable electrical signal:

[0050] 1. Optimization of mechanical contact:

[0051] The original high-resistance, unstable contact point is transformed into a low-resistance, reliable quasi-short-circuit point. At this point, even if the test voltage is not high, current can flow more easily through the point, generating a detectable leakage current or resistance change signal.

[0052] 2. Reduces the local breakdown threshold:

[0053] A diaphragm that requires a high voltage to break down under standard thickness and good condition can be broken down or significantly leaked with a relatively low test voltage in areas that are thinned by pressure and wrinkles. Pressure amplifies the electrical sensitivity of defects, making them "visible" at voltages that are safer (for good products).

[0054] 3. Stable and repeatable test conditions:

[0055] This eliminates random "missed detections" caused by poor contact, transforming the detection of structural defects from a probabilistic event into a deterministic one. Only when the pressure is stable are the optimized voltage and time parameters meaningful.

[0056] This is precisely the core of the invention's superiority over traditional methods: elevating "force" from a background condition to a core variable, and achieving universal and highly reliable detection of two main types of defects—physical short circuits (metallic foreign objects) and structural short circuits (wrinkles)—through three-dimensional synergistic optimization of "voltage-pressure-time".

[0057] Example 1

[0058] The application scenario is the Hi-pot short-circuit test of a square aluminum-cased lithium-ion battery core (model 50160119B-100Ah, using a 14μm base film separator) after the hot pressing process.

[0059] The specific steps are as follows:

[0060] S1. Construct a defective core sample library

[0061] (1) Substrate preparation: A batch of qualified cores is randomly selected from the normal mass production line. These cores all meet the mass production specifications. Specific information is as follows:

[0062] Select a sufficient number (e.g., 60) of the cores as experimental substrates.

[0063] (2) Artificial preparation of defective samples:

[0064] Group 1 - Normal Control Group: 10 cores were left untreated as a baseline for assessing whether the test parameters would cause damage.

[0065] Group 2 - Diaphragm Wrinkle (Folding) Defect Group: Twenty cores were selected to simulate physical defects in the diaphragm caused by improper winding or hot pressing. Using precision tools, diaphragm folds were controllably created in designated areas inside the cores, causing localized contact between the positive and negative electrodes through the folded diaphragm. To simulate different degrees of severity, the contact areas of the positive and negative electrodes caused by the folds were controlled with different gradients; for example, 10 contact areas of approximately 1*1 mm were prepared. 2 The sample, and 10 with a contact area of ​​approximately 2*2mm 2 The sample.

[0066] Group 3 - Metal Foreign Object Defect Group: Twenty battery cores were selected to simulate metal particle contamination in a production environment. 300-mesh iron powder, commonly found in battery equipment wear, was chosen as a typical metal foreign object. To ensure uniformity, controllability, and absence of interference from other substances, the iron powder was mixed with anhydrous ethanol to form a suspension. Further, micro-dispensing or spraying equipment was used to precisely introduce the suspension containing a fixed amount of iron powder into designated locations inside the core (such as the surface of the positive electrode, the middle of the separator, or the surface of the negative electrode). The anhydrous ethanol then rapidly evaporated, leaving only the iron powder particles. By controlling the suspension concentration and dispensing amount, different amounts of metal particle contamination were simulated.

[0067] Group 4 - Diaphragm Pinhole Defect Group: Ten cores were selected to simulate diaphragm perforation caused by electrode burrs or process damage. Using an ultra-fine laser or precision mechanical needle, through-holes with a diameter of approximately 0.2 mm were created on the core diaphragm.

[0068] S2. Using voltage, plane pressure, and test time as co-optimization parameters, conduct full factorial experiments on the defective core sample library and normal control samples under different parameter combinations to obtain defect detection rate datasets and normal core damage rate datasets.

[0069] (1) Experimental Platform: A pulse voltage drop short-circuit tester was selected as the testing equipment. This equipment can output an adjustable DC pulse voltage and accurately measure voltage drop and calculate parameters such as K value. At the same time, a precision pressure application platform (which can be driven by a servo motor) is integrated into the equipment to apply a uniform and precisely controllable planar pressure perpendicular to the large surface of the core placed on it. The pressure value can be displayed and recorded in real time (the unit is usually converted to N / mm). 2 The platform pressure control accuracy must be better than ±0.1 N / mm. 2 .

[0070] (2) Setting the experimental parameter range:

[0071] The voltage verification range is shown in the table below:

[0072]

[0073] The plane pressure verification range is shown in the table below:

[0074]

[0075] The test time verification interval is shown in the table below:

[0076]

[0077] (3) Other parameters:

[0078] To focus on the core variables, the voltage drop thresholds (such as VD1 and VD2) of the short-circuit tester were set to relatively lenient initial values ​​(e.g., VD1=10%, VD2=20%) in the experiment to avoid them affecting the detection judgment too early. These thresholds will then be precisely set based on the optimized core parameters.

[0079] (4) The experimental results are as follows ( Figs. 1-3 ):

[0080] Voltage verification is shown in the table below (surface pressure is 3.65 N / mm). 2 (Time 300ms, VD: 10%, VD2: 20%)

[0081] The plane pressure verification is shown in the table below (voltage set to 100V, time 300ms, VD: 10%, VD2: 20%):

[0082] The test time verification is shown in the table below (surface pressure is 3.65 N / mm). 2 (Voltage set to 100V, VD: 10%, VD2: 20%)

[0083] S3. Based on the defect detection rate dataset and the normal core damage rate dataset, with the optimization objective of the defect detection rate not being lower than the first threshold and the normal core damage rate not being higher than the second threshold, the optimal parameter combination is determined through data quantitative analysis.

[0084] in conclusion:

[0085] (1) The detection rate increases with the increase of the Hi-pot test voltage from 50V to 250V. When the test voltage is ≥100V, all short-circuit defective products can be identified and detected.

[0086] (2) Hi-pot test surface pressure from 1.82 N / mm 2 Increased to 4.87 N / mm 2 Test surface pressure ≥ 3.04 N / mm 2 All short-circuit defective products can be identified and detected;

[0087] (3) The Hi-pot test time was increased from 50ms to 400ms. The detection rate increased with the increase of test time. When the test time was 300ms, all short-circuit defective products could be identified and detected.

[0088] We discovered an intersection region of parameters that can simultaneously satisfy the dual objectives of "defect detection rate ≥ 99%" and "normal core damage rate = 0%". This intersection is defined by the critical points of each parameter: voltage ≥ 100V; time ≥ 300m / s; surface pressure ≥ 3.04N / mm. 2

[0089] Therefore, the final optimal parameter combination is:

[0090] 100V voltage;

[0091] Time 300m / s;

[0092] Surface pressure 3.04 N / mm 2 .

[0093] (4) For pulse voltage drop testers, an accurate K value (voltage decay rate) is needed to determine whether the voltage response of each test is a short circuit.

[0094] According to the formula for calculating the K value:

[0095] ;

[0096] Where △V is the attenuation voltage difference of the test voltage pulse, △T is the time interval between two open-circuit voltage tests; 0CV2 is the second battery open-circuit voltage, 0CV3 is the third battery open-circuit voltage, T2 is the test time for the second open-circuit voltage, and T3 is the test time for the third open-circuit voltage.

[0097] After determining the optimal parameter combination, use this parameter combination to test a large number (e.g., hundreds) of known normal cores and collect the K value for each sample.

[0098] Statistical analysis was performed on the K values ​​of these normal samples, such as... Fig. 4 As shown, the K value is mainly distributed below 0.022 mV / h.

[0099] To maximize the interception of micro-short circuit risks while avoiding misjudging normal fluctuations, the threshold for determining the pass / fail value of K is set to ≤0.022 mV / h. That is, if the K value calculated during the test is greater than this threshold, the core is determined to have a micro-short circuit risk.

[0100] S4. Import the optimal parameter combination into the production line for trial operation and verification, obtain the verification results, and determine the final target test parameters based on the verification results.

[0101] The specific steps are as follows:

[0102] a. Trial run import: The optimal parameter combination, K≤0.022 mV / h, and the corresponding voltage drop thresholds (VD: 10%, VD2: 20%) are programmed and imported into the Hi-pot test equipment on the production line.

[0103] b. Data Collection and Comparison: Select two consecutive production batches and conduct core testing using the new parameters. Simultaneously, retrieve data from the most recent batches produced using the old parameters (as the baseline for the "mass production group") for comparison. Monitor and statistically analyze the following indicators:

[0104] Core segment: Direct defect detection rate of online Hi-pot testing

[0105] Finished Battery Section: Perform K-value testing on the manufactured batteries and calculate the K-value defect rate.

[0106] c. Verification Results

[0107] A comparison of K-value defect rates revealed that the average K-value defect rate of the "experimental group" using the new parameters was approximately 0.02%.

[0108] The average defect rate of the K-value for the "mass production group" using the old parameters is approximately 0.09%.

[0109] Calculations show that the new parameters reduce the K-value defect rate (i.e., micro-short circuit risk) of the finished battery by approximately 78%.

[0110] Meanwhile, the online pass rate of normal cores under the new parameters was not affected, thus achieving the goal of "zero damage".

[0111] Example 2

[0112] Fig. 5 A structural block diagram of an apparatus for implementing the above method is shown.

[0113] This device can be implemented through software, hardware, or a combination of both, and can be integrated into the control computer of the process experiment platform or the MES (Manufacturing Execution System) of the production line.

[0114] The device includes a sample library construction module, a collaborative experiment module, a parameter optimization module, and a production verification module.

[0115] The sample library construction module is used to manage and guide step S1. It can be a software subsystem containing a defect preparation process library and a sample information database. Users can select defect types and gradient parameters through the interface. The module generates preparation guidance schemes and records the ID and defect attributes of each sample, forming an electronic sample library archive.

[0116] The collaborative experiment module is the core of experiment execution. It includes an experiment design submodule, which allows users to set the experimental range and step size for voltage, plane pressure, and test time; an equipment control submodule, which controls the Hi-pot tester and servo pressure platform through communication interfaces (such as PLC, RS485) and automatically switches parameters according to the experiment matrix; and a data acquisition submodule, which collects alarm results, raw waveforms, K values, and other data from the tester in real time and stores them in association with sample ID and experimental parameters.

[0117] The parameter optimization module is used for data analysis and decision-making. It obtains the complete experimental dataset from the collaborative experiment module and has built-in data analysis algorithms (such as trend fitting and multi-objective optimization algorithms). Users set optimization objectives (e.g., detection rate ≥99%, damage rate = 0%), and the module automatically analyzes the data and plots similar graphs. Figs. 1-3 The trend curve highlights the parameter regions that satisfy both objectives and recommends the optimal parameter combination. It also allows statistical analysis of normal sample K-value data for selected parameters, such as... Fig. 4 Recommended K-value threshold.

[0118] The production validation module is responsible for migrating and validating laboratory parameters to production. It can send the optimal parameter package output by the parameter optimization module to the production line testing equipment with one click. At the same time, it obtains production data (test results, finished product K-values, etc.) from the production line MES system in real time during the trial operation, performs statistical analysis and visualization, and automatically compares it with historical baseline data to generate a validation report, which helps determine whether the parameters can be finalized.

[0119] These modules can be directly purchased or customized to form an automated and intelligent closed-loop system for parameter development, from experimentation to production.

[0120] Example 3

[0121] Fig. 6 A schematic diagram of the hardware structure of a computer device that can be used to implement the present invention is shown.

[0122] Computer equipment includes buses, processors, memory, storage devices, input / output interfaces, and network interfaces.

[0123] A bus includes an address bus, a data bus, and a control bus, which are used to connect the various components of a computer device.

[0124] Processors (there may be multiple processors) can be central processing units (CPUs), graphics processing units (GPUs), or other forms of processing units with data processing and / or instruction execution capabilities, such as microprocessors (MCUs), programmable logic devices (FPGAs), etc., and can control other components in a computer device to perform desired functions.

[0125] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may execute the program instructions to implement the functions described in the above embodiments of the present invention and / or other desired functions. Various application programs and various data may also be stored in the computer-readable storage medium, such as sample library data, experimental datasets, optimization results, production verification data, etc.

[0126] Input / output interfaces are used to connect input / output modules to enable information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors (such as pressure sensor signals), etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0127] The network interface is used to connect the network communication module to enable communication with other devices or networks. For example, the network communication module can interact with lower-level control devices (testing instruments, pressure platforms), production line MES systems, database servers, etc., via wired means (such as USB, Ethernet cable) or wireless means (such as mobile networks, WIFI, Bluetooth).

[0128] Storage devices can be non-volatile storage devices such as hard disk drives and solid-state drives, used for long-term, large-capacity storage of programs and data.

[0129] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for optimizing short-circuit test parameters of a wound core, characterized in that, Includes the following steps: S1. Construct a defective core sample library; S2. Using voltage, plane pressure and test time as co-optimization parameters, conduct full factorial experiments on the defective core sample library and normal control samples under different parameter combinations to obtain defect detection rate dataset and normal core damage rate dataset. S3. Based on the defect detection rate dataset and the normal core damage rate dataset, with the defect detection rate not lower than the first threshold and the normal core damage rate not higher than the second threshold as the optimization objective, the optimal parameter combination is determined through data quantitative analysis. S4. Import the optimal parameter combination into the production line for trial operation and verification, obtain the verification results, and determine the final target test parameters based on the verification results.

2. The method for optimizing short-circuit test parameters of a wound core according to claim 1, characterized in that, The first threshold is 99%, and the second threshold is 0%.

3. The method for optimizing short-circuit test parameters of a wound core according to claim 1, characterized in that, The defective core sample library includes diaphragm wrinkle defect samples and metal foreign object defect samples.

4. The method for optimizing short-circuit test parameters of a wound core according to claim 1, characterized in that, The full factorial experiments with different parameter combinations described in step S2 include: Determine the plane pressure and test time, adjust the voltage within a preset range, and record the detection of defects and the damage of normal samples at each voltage point; Determine the voltage and test time, adjust the plane pressure within a preset range, and record the detection of defects and the damage of normal samples at each plane pressure point; Determine the plane pressure and voltage, adjust the test time within a preset range, and record the detection of defects and the damage to normal samples at each test time point.

5. The method for optimizing short-circuit test parameters of a wound core according to claim 4, characterized in that, The preset range of the voltage is ±50% of the mass production experience voltage value; the preset range of the planar pressure is the actual force range of the core assembly.

6. The method for optimizing short-circuit test parameters of a wound core according to claim 4, characterized in that, The preset test time range is 50ms to 400ms.

7. The method for optimizing short-circuit test parameters of a wound core according to claim 1, characterized in that, The optimal parameter combination includes: voltage ≥ 100V, plane pressure ≥ 3.04N / mm. 2 Test time ≥ 300ms.

8. The method for optimizing short-circuit test parameters of a wound core according to claim 1, characterized in that, It also includes the step of determining the judgment threshold K based on the optimal parameter combination, and the formula for calculating the value of K is: ； Where △V is the attenuation voltage difference of the test voltage pulse, △T is the time interval between two open-circuit voltage tests; 0CV2 is the second battery open-circuit voltage, 0CV3 is the third battery open-circuit voltage, T2 is the test time for the second open-circuit voltage, and T3 is the test time for the third open-circuit voltage.

9. A device for optimizing short-circuit test parameters of a wound core, characterized in that, include: The sample library component module is used to build a sample library of defective cores; The collaborative experiment module is used to perform full factorial experiments on the defective core sample library and normal control samples under different parameter combinations, using voltage, plane pressure and test time as collaborative optimization parameters, to obtain defect detection rate dataset and normal core damage rate dataset. The parameter optimization module, based on the defect detection rate dataset and the normal core damage rate dataset, takes the defect detection rate not lower than the first threshold and the normal core damage rate not higher than the second threshold as the optimization objective, and determines the optimal parameter combination through data quantitative analysis. The production verification module imports the optimal parameter combination into the production line for trial operation verification, obtains the verification results, and determines the final target test parameters based on the verification results.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

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