A method and system for testing the thermal stability of lithium battery recycling separators

Abstract

This application provides a method and system for testing the thermal stability of lithium-ion battery recycled separators. The method involves extracting effective data sequences of various evaluation indicators from multi-dimensional test data of lithium-ion battery recycled separator samples; determining the discrete characteristics of the effective data sequences corresponding to each evaluation indicator; determining correction coefficients for the contribution ratio of each evaluation indicator based on these discrete characteristics and the influence characteristics of each evaluation indicator on the lithium-ion battery recycled separator; classifying each evaluation indicator into thermal response indicators, mechanical retention indicators, and morphological stability indicators; and determining the temporal correlation law of these three types of indicators under thermal action; and determining the thermal stability index of the lithium-ion battery recycled separator sample based on the correction coefficients of all contribution ratios, the temporal correlation law, and the ambient temperature data of the lithium-ion battery recycled separator sample throughout the entire process. Using this method, the thermal stability of lithium-ion battery recycled separators can be tested based on the correlation between various performance characteristics.

Description

Technical Field

[0001] This application relates to the field of thermal stability testing technology, and more specifically, to a method and system for testing the thermal stability of lithium battery recycling separators. Background Technology

[0002] Thermal stability testing is an analytical technique for evaluating a material’s ability to resist physical changes and chemical decomposition under elevated temperatures. Its core purpose is to determine key performance indicators such as decomposition temperature, glass transition temperature, and heat distortion temperature of a sample during heating.

[0003] As a core component of lithium-ion batteries, the separator is the key structure that prevents direct contact between the positive and negative electrodes and avoids internal short circuits. Its stability under high-temperature environments directly determines the safety and lifespan of the battery. With the rapid growth of retired lithium-ion batteries in the new energy industry, the resource utilization of recycled separators has become a key focus of industry development, and thermal stability is the core indicator for determining whether recycled separators can be reused in battery manufacturing.

[0004] Due to the complex sources of raw materials, the differences in the cycling conditions of retired batteries, and the fluctuations in dismantling and regeneration processes, recycled separators exhibit poor performance consistency. Traditional testing methods often directly adopt the single-performance dimension testing methods used for new separators. On the one hand, this fails to fully consider the inherent causal relationship between the thermal response, morphological deformation, and mechanical degradation of the separator under thermal effects, making it difficult to comprehensively reflect the thermal failure risk of recycled separators in practical applications. On the other hand, the lack of an adaptive evaluation system for the poor consistency of recycled separators makes it impossible to effectively quantify the impact of data dispersion on the evaluation results. This leads to a significant deviation between the thermal stability evaluation results and actual application scenarios, making it difficult to accurately reflect the safety and reliability during high-temperature use. This restricts the large-scale promotion and application of recycled separators. Therefore, how to test the thermal stability of recycled lithium battery separators based on the correlation between various performance characteristics has become a problem facing the industry. Summary of the Invention

[0005] This application provides a method and system for testing the thermal stability of lithium battery recycling membranes, which can test the thermal stability of lithium battery recycling membranes based on the correlation between various properties in the lithium battery recycling membrane.

[0006] Firstly, this application provides a method for testing the thermal stability of lithium battery recycled separators. The method involves pre-treating the lithium battery recycled separator samples and establishing a standardized testing environment. This testing environment includes a high and low temperature environmental test chamber, a universal testing machine, a non-contact laser length measuring instrument, and an ambient temperature sensor. After calibrating the testing system corresponding to the testing environment, the thermal stability test is conducted. The method includes the following steps: The real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycled separator sample in the testing system are obtained to form multi-dimensional test data of the lithium battery recycled separator sample. The evaluation indicators for thermal stability evaluation based on the lithium battery recycled separator sample are extracted from the multi-dimensional test data to obtain the effective data sequence of each evaluation indicator. The discrete characteristics of the effective data sequence corresponding to each evaluation index are determined. Based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling membrane, the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling membrane sample is determined. The evaluation indicators are divided into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and the temporal correlation law of the three types of indicators under thermal action is determined. The ambient temperature data of the lithium battery recycled separator sample in the test environment is collected by an ambient temperature sensor. The thermal stability of the lithium battery recycled separator sample is coupled and analyzed based on the correction coefficient of all contribution proportions, the time-series correlation law and the ambient temperature data throughout the test environment to obtain the thermal stability index of the lithium battery recycled separator sample.

[0007] In some embodiments, the extraction of effective data sequences for each evaluation index from the multi-dimensional test data for thermal stability evaluation based on the recycled lithium battery separator sample specifically includes: The various evaluation indicators for thermal stability evaluation of the lithium battery recycled separator sample were obtained. Anomaly processing is performed on the multi-dimensional test data to obtain anomaly-processed multi-dimensional test data; The effective data sequences of each evaluation index are extracted from the multi-dimensional test data after the anomaly handling.

[0008] In some embodiments, determining the discrete characteristics of the effective data sequence corresponding to each evaluation index specifically includes: Calculate the standard deviation, coefficient of variation, and range of the effective data series corresponding to each evaluation index; Each standard deviation, coefficient of variation, and range is used as a discrete feature of the corresponding valid data sequence.

[0009] In some embodiments, the correction coefficient for determining the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample, based on the influence characteristics of each discrete feature and each evaluation index on the lithium battery recycled separator, specifically includes: Based on the thermal failure mechanism and engineering application core of lithium battery recycling membrane, the influence characteristics of each evaluation index on the lithium battery recycling membrane are determined. By coupling the discrete features and all the influencing features, the initial correction coefficients of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample are obtained. All initial correction coefficients are calibrated to obtain the correction coefficients for the contribution of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample.

[0010] In some embodiments, determining the temporal correlation patterns of the three types of indicators under thermal action specifically includes: Obtain historical monitoring data corresponding to the three types of indicators; All historical monitoring data are time-aligned to obtain historical aligned monitoring data; The temporal correlation patterns of the three types of indicators under thermal action were determined using the historical alignment monitoring data.

[0011] In some embodiments, the thermal stability of the lithium battery recycled separator sample is coupled and analyzed based on the correction coefficients of all contribution proportions, the time-series correlation law, and the ambient temperature data throughout the process, to obtain the thermal stability index of the lithium battery recycled separator sample, specifically including: Obtain the preset temperature rise curve of the lithium battery recycled separator sample; Determine the temperature deviation between the ambient temperature data throughout the process and the preset temperature rise curve; Determine the contribution percentage of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample. The corresponding contribution percentages are adjusted based on the adjustment coefficients of all contribution percentages to obtain the adjusted contribution percentages. Based on the contribution percentages of all corrected data sequences and the temperature deviations, a preliminary thermal stability index of the lithium battery recycled separator sample is obtained by performing a fusion analysis. The initial thermal stability index is corrected based on the time-series correlation law to obtain the thermal stability index of the lithium battery recycled separator sample.

[0012] In some embodiments, the multidimensional test data includes real-time temperature data at different monitoring points on the sample surface, longitudinal and transverse tensile strength data, and length and width dimensional data.

[0013] Secondly, this application provides a thermal stability testing system for lithium battery recycled separators. The system includes pre-treatment of lithium battery recycled separator samples and the establishment of a standardized testing environment. This testing environment is equipped with a high and low temperature environmental test chamber, a universal testing machine, a non-contact laser length measuring instrument, and an ambient temperature sensor. After calibration of the testing system corresponding to the testing environment, thermal stability testing is conducted. The system includes: The acquisition module is used to acquire real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycled separator sample in the testing system, forming multi-dimensional test data of the lithium battery recycled separator sample; The processing module is used to extract the effective data sequence of each evaluation index from the multi-dimensional test data for evaluating the thermal stability of the lithium battery recycled separator sample. The processing module is also used to determine the discrete characteristics of the effective data sequence corresponding to each evaluation index, and to determine the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling separator sample based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling separator. The processing module is also used to classify the various evaluation indicators into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and to determine the temporal correlation law of the three types of indicators under thermal action. The execution module is used to collect the ambient temperature data of the test scenario in which the lithium battery recycled separator sample is located through an ambient temperature sensor, and to perform coupled analysis on the thermal stability of the lithium battery recycled separator sample based on the correction coefficients of all contribution proportions, the time-series correlation law, and the ambient temperature data throughout the test scenario, so as to obtain the thermal stability index of the lithium battery recycled separator sample.

[0014] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for testing the thermal stability of lithium battery recycling separators.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for testing the thermal stability of lithium battery recycling separators.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The lithium battery recycling separator thermal stability testing method and system provided in this application first acquires real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycling separator sample in the testing system, forming multi-dimensional test data of the lithium battery recycling separator sample; based on the evaluation index for thermal stability evaluation of the lithium battery recycling separator sample, the effective data sequence of each evaluation index is extracted from the multi-dimensional test data; the discrete characteristics of the effective data sequence corresponding to each evaluation index are determined, and the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling separator sample is determined according to the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling separator; the evaluation index is divided into thermal response index, mechanical retention index, and morphological stability index, and the temporal correlation law of the three types of indexes under thermal action is determined; the ambient temperature data of the entire test scenario of the lithium battery recycling separator sample is collected by an ambient temperature sensor, and the thermal stability of the lithium battery recycling separator sample is coupled and analyzed based on the correction coefficients of all contribution ratios, the temporal correlation law, and the entire ambient temperature data to obtain the thermal stability index of the lithium battery recycling separator sample.

[0017] Therefore, this application, in the process of testing the thermal stability of lithium battery recycling membranes, firstly acquires multi-dimensional test data covering real-time ambient temperature, tensile force and strain, and dimensional deformation. This comprehensively covers the core performance dimensions of lithium battery recycling membrane thermal stability evaluation, providing complete and authentic raw data support for subsequent evaluation and analysis, avoiding the analytical limitations caused by a single data dimension. Secondly, it selectively extracts effective data sequences matching the thermal stability evaluation indicators from the multi-dimensional test data, eliminating redundant and irrelevant data, reducing the interference of invalid data on subsequent analysis, and ensuring the relevance and effectiveness of subsequent evaluation and analysis. Thirdly, by determining the discrete characteristics of each indicator's data and calculating the contribution ratio correction coefficient based on the actual impact characteristics of the indicators on the lithium battery recycling membrane, it can balance the degree of core engineering impact of each indicator with the credibility differences caused by the discrete characteristics of the data itself, providing a scientific and reasonable basis for weight correction in subsequent evaluations, making the evaluation process more realistic. The testing process involved classifying evaluation indicators into three categories and clarifying their temporal correlation patterns under thermal effects. This clearly reveals the inherent dynamic correlations between different types of indicators and the sequential logic of changes under thermal effects, aligning with the actual thermal failure mechanism of lithium battery recycling separators. This avoids the one-sidedness caused by isolated analysis of individual indicators and provides a realistic basis for subsequent coupled analysis. Thermal stability coupled analysis was conducted by combining contribution ratio correction coefficients, the temporal correlation patterns of the three types of indicators, and full-process environmental temperature data. This not only scientifically corrects the contribution ratios of each indicator through correction coefficients but also incorporates the temporal correlation characteristics between indicators. Furthermore, relying on full-process environmental temperature data eliminates the interference of environmental temperature fluctuations in the test scenario on the evaluation results, achieving a comprehensive coupled evaluation of multiple factors and dimensions. The resulting thermal stability index accurately and comprehensively reflects the actual thermal stability performance of lithium battery recycling separator samples, significantly improving the scientific validity, accuracy, and engineering reference value of the test results. Using the above scheme, the thermal stability of lithium battery recycling separators was tested based on the correlation relationships between various performance characteristics. Attached Figure Description

[0018] Figure 1 This is an exemplary flowchart of a lithium battery recycling membrane thermal stability test method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of discrete features according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of temporal correlation patterns according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a lithium battery recycling separator thermal stability testing system according to some embodiments of this application; Figure 5This is a schematic diagram of the structure of a computer device for implementing a method for testing the thermal stability of a lithium battery recycling separator, according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a method for testing the thermal stability of a lithium battery recycling separator according to some embodiments of this application. The method mainly includes the following steps: In some embodiments, the pretreatment of lithium battery recycled separator samples and the establishment of a standardized testing environment are completed in advance. After the testing system corresponding to the testing environment is calibrated, thermal stability testing can be carried out in the following manner: The pretreatment of lithium battery recycled separator samples and the establishment of a standardized testing environment are completed in advance according to GB / T 36363-2018 standard; Sample pretreatment: Select recycled separator samples obtained from dismantled retired lithium batteries, and perform three ultrasonic immersion cleanings with N-methylpyrrolidone to remove residual electrolyte, lithium salt, binder, and conductive agent impurities from the surface. Infrared spectroscopy is used to detect the residual impurities in the samples, ensuring that the residual impurities are less than 0.5%. The samples are then dried in a vacuum drying oven at 120℃ for 4 hours. After removal, they are placed in a standard environment at 23℃±2℃ and 50%±5% relative humidity for 24 hours to remove surface wrinkles. According to standard requirements, the samples are cut into three independent parallel samples: heat shrinkage test samples, longitudinal mechanical property test samples, and transverse mechanical property test samples. At least 5 samples of each type are prepared. One parallel sample; Test environment setup and calibration: The test environment includes a high and low temperature environment test chamber (temperature uniformity ≤ ±0.2℃, temperature fluctuation ≤ ±0.1℃), a universal material testing machine (accuracy grade 0.5), a non-contact laser length measuring instrument (measurement accuracy ≤ 0.01mm), and A... A high-precision platinum resistance temperature sensor was used. Before testing, all equipment was calibrated. The temperature sensor was fixed in the sample testing area inside the high and low temperature test chamber, avoiding the heating tube outlet, to ensure a uniform temperature field in the testing area. The measuring optical path of the non-contact laser length measuring instrument was aligned with the test gauge point of the heat shrinkage test specimen, without contact or constraint. The longitudinal and transverse mechanical test specimens were clamped in the special fixtures of the universal testing machine. After clamping, the specimens were in a naturally straight state without pre-tension, and the clamping position and gauge line met the standard requirements. Test program settings: A preset heating curve was set according to the test target, including an initial temperature of 25℃, a heating rate of 5℃ / min, key isothermal nodes and corresponding isothermal durations, and an ending temperature of 200℃. During the test, all equipment was simultaneously turned on, and all test data were collected synchronously at a frequency of 1Hz. Data, corresponding temperature nodes, and timestamps were recorded throughout the process until the heating program ended and the test was completed.

[0021] In step 101, the real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycling separator sample in the test system are obtained to form multi-dimensional test data of the lithium battery recycling separator sample.

[0022] It should be noted that the multi-dimensional test data in this application represents a comprehensive data set of performance dimensions of lithium battery recycled separator samples during thermal stability testing. The multi-dimensional test data includes real-time ambient temperature data of the uniform temperature field inside the test chamber, real-time tensile and strain data of the separator sample in the longitudinal and transverse directions, real-time non-contact dimensional deformation data of the length and width, and temperature nodes and timestamps corresponding to all data. The real-time ambient temperature data includes the temperature values ​​and temperature change rates at each point in the test area. The real-time tensile and strain data in the longitudinal and transverse directions includes real-time load values, real-time displacement values, and strain changes. The real-time dimensional deformation data of the length and width includes real-time gauge length values ​​and thermal shrinkage deformation. It can be used to analyze the thermal response law, morphological stability characteristics, and mechanical property decay law of the separator sample under temperature during the test.

[0023] In step 102, the effective data sequences of each evaluation index for thermal stability evaluation based on the lithium battery recycled separator sample are extracted from the multi-dimensional test data.

[0024] In some embodiments, the extraction of effective data sequences for each evaluation index from the multi-dimensional test data for thermal stability evaluation based on the recycled lithium battery separator sample can be achieved using the following steps: The various evaluation indicators for thermal stability evaluation of the lithium battery recycled separator sample were obtained. Anomaly processing is performed on the multi-dimensional test data to obtain anomaly-processed multi-dimensional test data; The effective data sequences of each evaluation index are extracted from the multi-dimensional test data after the anomaly handling.

[0025] It should be noted that the evaluation indicators in this application include thermal response indicators: rate of temperature change, real-time tolerance temperature; morphological stability indicators: thermal shrinkage rate, dimensional uniformity; and mechanical retention indicators: tensile strength retention rate, elongation at break, and maximum tolerance temperature. The evaluation indicators represent the core parameter set of the thermal stability of lithium battery recycling separators, reflecting the thermal response law, mechanical performance retention ability, and morphological stability characteristics of the separator under high temperature conditions.

[0026] In specific implementation, the multi-dimensional test data is anomaly processed to obtain the anomaly-processed multi-dimensional test data. This can be achieved by using the 3-standard-deviation method to process the multi-dimensional test data. First, the mean and standard deviation of each data dimension are calculated. The normal data range is set as [mean - 3 standard deviation, mean + 3 standard deviation]. The data is traversed to remove outliers that exceed the range. Missing values ​​caused by communication interruption are filled in using linear interpolation to obtain the anomaly-processed multi-dimensional test data.

[0027] In addition, in specific implementation, the effective data sequence of each evaluation index extracted from the multi-dimensional test data after anomaly processing can be achieved in the following way: For the rate of temperature change, extract the sample temperature data corresponding to all timestamps, calculate the rate value of each time interval according to (temperature of the next node - temperature of the previous node) / (time of the next node - time of the previous node), and arrange them in chronological order to form a temperature rate of change sequence; For the real-time tolerance temperature, extract the temperature data of each heating node and the corresponding diaphragm morphology observation records, screen out the real-time tolerance temperature values ​​when the diaphragm does not melt or rupture at each node, and arrange them in the order of heating nodes to form a real-time tolerance temperature sequence; For the highest tolerance temperature, extract the maximum value in the real-time tolerance temperature sequence as the highest tolerance temperature feature value of the sample; For the tensile strength retention rate, extract the initial tensile strength data at room temperature and the real-time tensile strength data of each heating node, calculate the retention rate according to (real-time tensile strength / initial tensile strength) × 100%, and sort them in the order of heating nodes to form a tensile strength retention rate sequence; For the elongation at break, extract the length data of the sample at the moment of fracture and the initial length data at each heating node, and calculate the retention rate according to (length after fracture - ... The elongation rate is calculated as (initial length - real-time size) / initial length × 100%, and sorted by heating node to form a sequence of elongation at break. For thermal shrinkage, the initial length and width data at room temperature and the real-time length and width data at each heating node are extracted. The length and width shrinkage rates are calculated as (initial size - real-time size) / initial size × 100%, and sorted by heating node to form a sequence of thermal shrinkage rates. For dimensional uniformity, the dimensional data of five points (four corners and center) of the sample at each heating node are extracted. The standard deviation of the dimensional data at each node is calculated and divided by the average value to obtain the coefficient of variation. The coefficient of variation is sorted by heating node to form a sequence of dimensional uniformity. Based on the above method, the effective data sequences of each evaluation index are extracted from the multi-dimensional test data after anomaly processing.

[0028] It should be noted that the valid data sequence in this application represents the valid quantitative data set of each thermal stability evaluation index extracted from multi-dimensional test data, and each sequence uniquely corresponds to a core evaluation index; reflecting the continuous dynamic change law of the corresponding evaluation index as the test process progresses.

[0029] In step 103, the discrete characteristics of the effective data sequence corresponding to each evaluation index are determined, and the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator is determined based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycled separator sample.

[0030] In some embodiments, reference Figure 2As shown, this figure is an exemplary flowchart of determining discrete features in some embodiments of this application. In this embodiment, determining the discrete features of the effective data sequence corresponding to each evaluation index can be achieved by the following steps: In step 1031, the standard deviation, coefficient of variation, and range of the effective data series corresponding to each evaluation index are calculated; In step 1032, each standard deviation, coefficient of variation, and range is used as a discrete feature of the corresponding valid data sequence.

[0031] In practice, one evaluation indicator is selected as the chosen evaluation indicator, and the corresponding effective data sequence is selected. For the chosen evaluation indicator, when calculating the standard deviation, the mean of the sequence is first obtained by using the arithmetic mean method, and then the sum of squares of the deviations of each data point from the mean is calculated. The mean square value is calculated by taking the degrees of freedom in combination with the statistical characteristics of the sample, and the square root of the mean square value is taken to obtain the standard deviation. The standard deviation reflects the degree of concentrated fluctuation of the data from the mean. When calculating the coefficient of variation, the ratio of the obtained standard deviation to the mean of the sequence is used as the coefficient of variation. The coefficient of variation reflects the relative dispersion of the data. When calculating the range, the maximum and minimum values ​​are selected by traversing the sequence values, and the range is obtained by subtracting the minimum value from the maximum value. The range reflects the fluctuation range of the sequence data. Finally, the standard deviation, coefficient of variation, and range of the effective data sequence corresponding to the chosen evaluation indicator are obtained. The standard deviation, coefficient of variation, and range of the effective data sequence corresponding to the remaining evaluation indicators are then determined.

[0032] It should be noted that the discrete features in this application represent the characteristics of the discreteness of the effective data sequence corresponding to each evaluation index of the thermal stability of lithium battery recycling separator, reflecting the dynamic change stability of the corresponding evaluation index. That is, the larger the value of the discrete feature parameter, the more drastic the fluctuation of the index data during the test process.

[0033] In some embodiments, determining the correction coefficient for the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample, based on the influence characteristics of each discrete feature and each evaluation index on the lithium battery recycled separator, can be achieved through the following steps: Based on the thermal failure mechanism and engineering application core of lithium battery recycling membrane, the influence characteristics of each evaluation index on the lithium battery recycling membrane are determined. By coupling the discrete features and all the influencing features, the initial correction coefficients of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample are obtained. All initial correction coefficients are calibrated to obtain the correction coefficients for the contribution of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample.

[0034] In practical implementation, based on the thermal failure mechanism and core engineering application of lithium battery recycling membranes, the influence characteristics of each evaluation index on the lithium battery recycling membrane can be determined in the following way: First, combining the thermal failure law of lithium battery recycling membranes under high temperature environment, the actual influence degree of each evaluation index on the thermal stability of the membrane is clarified. Then, the influence characteristics of each evaluation index are quantified and assigned using the 1-9 scale method, where scale 1 indicates that the influence of two evaluation indices on the thermal stability of the membrane is equally important, scale 3 indicates that one index is slightly more important than the other, and scale 5 indicates that one index is significantly more important than the other. Scale 7 indicates that one indicator is significantly more important than another, and scale 9 indicates that one indicator is extremely important than another. Scales 2, 4, 6, and 8 are used as intermediate values ​​between adjacent scales. After completing pairwise comparisons and scale assignments for all evaluation indicators, the assignment results for each indicator are summarized, and the results are normalized by using an arithmetic mean to eliminate the dimensional differences caused by different scale assignments, thus obtaining the quantitative value of the influence characteristics of each evaluation indicator. This quantitative value is a dimensionless value that only reflects the relative differences in the degree of influence of each indicator. Other methods can be used in other embodiments, which are not limited here.

[0035] In addition, in specific implementation, the initial correction coefficients for the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample can be obtained by coupling the discrete features and all influencing features. This can be achieved as follows: the values ​​of the parameters corresponding to each discrete feature are mapped to the range of 0-1, eliminating the dimensional differences between different discrete feature parameters. After normalizing the three types of discrete feature parameters, a fusion calculation is performed separately for each evaluation index. First, the specific values ​​of the standard deviation, coefficient of variation, and range of the three discrete feature parameters obtained after extreme value normalization are extracted. Then, the three normalized values ​​are summed using an equal-weighted arithmetic average. The sum is divided by three, and the calculated average value is the discrete feature comprehensive value of the evaluation index. This comprehensive value is also mapped to the range of 0-1; a larger value indicates a higher degree of data dispersion for the corresponding index. Subsequently, based on the actual engineering requirements of the thermal stability evaluation of the lithium battery recycled separator, corresponding weight coefficients are set for the quantitative value of the influencing features and the discrete feature comprehensive value, with both weight coefficients ranging from 0 to 1. The values ​​between the two are taken as the sum of the two values, and the weight coefficients should be set according to the principle that the influence characteristics of the indicators in engineering practice are the core reference. Then, based on the set weight coefficients, coupling operations are carried out. The quantitative value of the influence characteristic is multiplied by the corresponding weight coefficient. At the same time, the discrete feature comprehensive value 1 - the discrete feature comprehensive value is reverse normalized and multiplied by the corresponding weight coefficient. The results of the two multiplications are summed to obtain the initial correction coefficient of the contribution ratio of each evaluation indicator. Other methods can be used in other embodiments, which are not limited here.

[0036] In addition, in specific implementation, calibrating all initial correction coefficients to obtain the correction coefficients for the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample can be achieved in the following way: First, perform extreme value normalization on the initial correction coefficients of all evaluation indicators, mapping the values ​​of all initial correction coefficients to a reasonable range of 0.1-1.0. Setting this range can avoid the problem of the indicator weight being ineffective due to the correction coefficient being too small, or the indicator weight being excessively amplified due to the correction coefficient being too large. Then, calculate the arithmetic mean of all normalized initial correction coefficients, and at the same time calculate the absolute difference between the initial correction coefficient of a single evaluation indicator and this average value. Divide the absolute difference by the average value to obtain the individual indicator's correction coefficient. The deviation rate is pre-set with an engineering threshold. If the deviation rate of a single evaluation indicator exceeds this threshold, the initial correction coefficient of that indicator is reduced proportionally according to the degree of dispersion, based on the calculated arithmetic mean, until the deviation rate is below the engineering threshold. During the fine-tuning process, the initial correction coefficients of other indicators remain unchanged. After adjusting all indicators exceeding the threshold, the correction coefficient of each evaluation indicator is obtained, representing the contribution of each indicator to the thermal stability evaluation of lithium battery recycled separator samples. The pre-set deviation rate engineering threshold is determined by combining the performance characteristics of lithium battery recycled separators with the practical engineering requirements of thermal stability testing. Statistical analysis of test data from multiple batches of recycled separator samples from different raw material sources and different recycling processes shows that the deviation rate of the evaluation indicator correction coefficients for more than 90% of the samples is below 20%. Therefore, the engineering threshold for the deviation rate is set to 20%. If the deviation rate of a single evaluation indicator exceeds 20%, it indicates that the data dispersion of that indicator exceeds the normal engineering range, and its correction coefficient needs to be reduced according to the degree of dispersion. This ensures data reliability while also taking into account the actual testing characteristics of poor performance consistency of recycled separators. Other methods can be used in other embodiments, which are not limited here.

[0037] It should be noted that the influence characteristics in this application represent the importance of each evaluation index to the thermal stability evaluation of lithium battery recycling separators, reflecting the differences in the criticality of different indicators in inducing separator thermal failure and ensuring core thermal stability performance; the initial correction coefficient reflects the preliminary correction tendency and magnitude of the basic contribution ratio of the indicators based on the degree of engineering influence of the evaluation indicators on the thermal stability of lithium battery recycling separators and their own data dispersion characteristics, and can be used as the original calculation basis for the correction of the contribution ratio of the evaluation indicators; the correction coefficient reflects the degree and standard of correction of the basic contribution ratio of the indicators, and can be directly used to correct the basic contribution ratio of each evaluation indicator.

[0038] In step 104, the evaluation indicators are divided into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and the temporal correlation law of the three types of indicators under thermal action is determined.

[0039] It should be noted that the thermal response index in this application represents the membrane's response to temperature and its tolerance; the mechanical retention index represents the membrane's mechanical load-bearing capacity and resistance to damage under high temperature conditions; and the morphological stability index represents the membrane's dimensional retention and structural stability under high temperature conditions. Preferably, in some embodiments, the evaluation indices are divided into thermal response indices, mechanical retention indices, and morphological stability indices, and the indices are classified according to their attributes: the rate of temperature change and the maximum tolerance temperature, which reflect dynamic temperature changes and ultimate tolerance, are classified into thermal response indices; the tensile strength retention rate and elongation at break, which characterize mechanical performance degradation and deformation capacity, are classified into mechanical retention indices; and the thermal shrinkage rate and dimensional uniformity, which reflect dimensional deformation and distribution consistency, are classified into morphological stability indices. After classification, the thermal failure mechanism of lithium battery recycled membranes is compared with that of other membranes, such as thermal performance degradation first causing morphological deformation, which in turn leads to mechanical performance collapse. The correlation and independence of each layer of indices are checked to ensure that no indices are classified repeatedly and no core performance dimensions are omitted, and finally the classification of thermal response indices, mechanical retention indices, and morphological stability indices is completed.

[0040] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the time-series correlation pattern in some embodiments of this application. In this embodiment, the determination of the time-series correlation pattern of three types of indicators under thermal action can be achieved by the following steps: In step 1041, historical monitoring data corresponding to the three types of indicators are obtained; In step 1042, all historical monitoring data are time-aligned to obtain historical aligned monitoring data; In step 1043, the temporal correlation patterns of the three types of indicators under thermal action are determined using the historical alignment monitoring data.

[0041] In practice, historical monitoring data of the evaluation indicators corresponding to each indicator are extracted from the historical database of high and low temperature tests in the laboratory. The historical monitoring data includes the quantitative value of the evaluation indicator, the collection timestamp, and the sampling spatial location information to ensure that the data covers the test scenarios of diaphragm samples with different heating rates and different initial states.

[0042] In addition, in specific implementation, all historical monitoring data are time-aligned to obtain historical aligned monitoring data. This can be achieved in the following way: all historical monitoring data are time-aligned using a timestamp synchronization method. The acquisition frequency of the ambient temperature sensor for the thermal response index is used as the reference time axis. Low-frequency data for mechanical stability index and morphological stability index are supplemented using linear interpolation. Time deviations caused by sensor response delays are eliminated by time offset correction, so that each index has corresponding index data and spatial location data at the same time node, forming historical aligned monitoring data. Other alignment methods can also be used in other embodiments, which are not limited here.

[0043] In addition, in specific implementation, the determination of the temporal correlation of the three types of indicators under thermal action through the historical alignment monitoring data can be achieved in the following way: Based on the historical alignment monitoring data, the coupling relationship is analyzed from the time and space dimensions. In the time dimension, the trend inflection point analysis method is used to extract the time inflection points when each indicator shows significant changes by traversing the historical alignment monitoring data. That is, a dual judgment threshold is set: the statistical threshold adopts the rule of 3 times the standard deviation, corresponding to low probability fluctuation events, and the engineering threshold refers to the lithium battery separator industry standard and the thermal failure critical value. For example, the thermal shrinkage rate failure critical value is 10%, and the engineering threshold is set to a single change amount ≥5%; the tensile strength retention rate failure critical value is 70%, and the engineering threshold is set to a continuous decrease amount ≥10%, to ensure that the judgment fits the actual application scenario; then, the sliding window method is used to traverse the full historical alignment data, and the difference between the mean of the indicator in the current window and the mean of the previous window is calculated. If the difference simultaneously meets the requirements of exceeding the statistical threshold and exceeding the engineering threshold, it is initially judged as a significant change. The time difference between the inflection points of the thermal response index and the morphological stability index, and the morphological stability index and the mechanical retention index are calculated, and the time difference is processed to be dimensionless (time difference / The total test duration is used to determine the temporal sequence and lag of thermal performance changes, morphological performance responses, and mechanical performance degradation. Spatially, a point data matching method is used to associate the temperature data of thermal response indicators at each time point with the corresponding morphological and mechanical data. By calculating the coefficient of variation of performance data at different points at the same time, the spatial correspondence between local high-temperature points and local morphological deformation and mechanical attenuation is clarified. The relationships obtained in the time and spatial dimensions are used as the temporal correlation rules of each indicator under thermal action. Other methods can be used to determine these in other embodiments, which are not limited here.

[0044] It should be noted that the historical aligned monitoring data in this application refers to a standardized data set formed after time alignment processing of the historical monitoring data corresponding to thermal response index, mechanical retention index, and morphological stability index. This reflects the synchronous dynamic change state of each index in the same time dimension, ensuring that the data of different indices are comparable in the spatiotemporal dimensions. The temporal correlation law represents the correlation law between each index in the time and spatial dimensions. The temporal correlation law reflects the temporal order, lag time, and spatial correspondence of the changes of each index under thermal action, intuitively reflecting the interaction logic and influence path of each performance dimension in thermal stability evaluation.

[0045] In step 105, ambient temperature data of the test environment of the lithium battery recycling membrane sample is collected by an ambient temperature sensor. The thermal stability of the lithium battery recycling membrane sample is coupled and analyzed based on the correction coefficients of all contribution proportions, the time-series correlation law, and the ambient temperature data to obtain the thermal stability index of the lithium battery recycling membrane sample.

[0046] In practice, the ambient temperature sensor is fixed inside the high and low temperature test chamber, 5-10 cm away from the lithium battery recycling membrane sample and away from the heating tube outlet. This ensures that the sensor does not directly contact the sample and is in a uniform temperature field around the sample. During the test, the ambient temperature sensor and the sample sensor are turned on simultaneously to collect the ambient temperature data of the test scenario in real time and record the corresponding timestamps, ensuring that they correspond one-to-one with the temperature, mechanical, and dimensional data of the sample. The ambient temperature data throughout the test reflects the actual temperature background of the test scenario, which can be used to verify the accuracy of the test chamber heating program and to eliminate the interference of ambient temperature fluctuations on the sample's own temperature measurement and performance data, ensuring that changes in sample performance are attributed to its own thermal stability rather than environmental interference.

[0047] In some embodiments, the thermal stability of the lithium battery recycled separator sample is coupled and analyzed based on the correction coefficients of all contribution proportions, the time-series correlation law, and the ambient temperature data throughout the process. The thermal stability index of the lithium battery recycled separator sample can be obtained by the following steps: Obtain the preset temperature rise curve of the lithium battery recycled separator sample; Determine the temperature deviation between the ambient temperature data throughout the process and the preset temperature rise curve; Determine the contribution percentage of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample. The corresponding contribution percentages are adjusted based on the adjustment coefficients of all contribution percentages to obtain the adjusted contribution percentages. Based on the contribution percentages of all corrected data sequences and the temperature deviations, a preliminary thermal stability index of the lithium battery recycled separator sample is obtained by performing a fusion analysis. The initial thermal stability index is corrected based on the time-series correlation law to obtain the thermal stability index of the lithium battery recycled separator sample.

[0048] It should be noted that the preset temperature rise curve in this application represents a temperature-time correlated standard temperature rise curve formulated for the purpose of thermal stability testing of lithium battery recycling separators; it reflects the temperature control logic of thermal stability testing; the preset temperature rise curve includes the starting temperature, preset temperature rise rate, key isothermal nodes and corresponding isothermal duration, and termination temperature.

[0049] In specific implementation, the temperature deviation between the entire ambient temperature data and the preset heating curve can be determined in the following way: The temperature deviation is calculated using a timestamp comparison method. The actual ambient temperature value corresponding to the same timestamp as the preset heating curve in the entire ambient temperature data is extracted. The absolute temperature deviation at each time point is calculated using the formula ΔT = |Tactual - Tpreset|, where Tactual is the actual ambient temperature in the entire ambient temperature data, and Tpreset is the preset temperature at the corresponding timestamp in the preset heating curve. Simultaneously, the average deviation of three consecutive time points is calculated. The absolute temperature deviation and the average deviation are used as the temperature deviation between the entire ambient temperature data and the preset heating curve. Here, the temperature deviation represents the degree of deviation between the ambient temperature and the preset curve during the test. In other embodiments, other methods can also be used to determine this deviation, which are not limited here.

[0050] Furthermore, in specific implementation, the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample can be determined as follows: First, combining the thermal failure mechanism of the lithium battery recycled separator with the core thermal safety requirements in practical engineering applications, clarify the actual impact of each evaluation index on the thermal stability of the separator. Then, use the 1-9 scale method to perform pairwise comparisons and quantification of all evaluation indices. Based on the importance comparison results between each index, construct a coupled judgment matrix that satisfies reflexivity and reciprocity. Summate each column element of this matrix to obtain the column sum. Then, divide each element in the matrix by the column sum of its corresponding column to obtain the normalized judgment matrix. Finally, calculate the arithmetic mean of each row of the normalized judgment matrix to obtain the initial weight vector of each evaluation index. Subsequently, a consistency check is performed on the initial weight vector. First, the product of the coupling judgment matrix and the initial weight vector is calculated. Then, the ratio of each element in the product to the corresponding initial weight is calculated and the average value is obtained to obtain the maximum eigenvalue of the matrix. The consistency index is calculated based on the maximum eigenvalue. The corresponding value is then obtained by referring to the industry-known random consistency index table. The consistency ratio is calculated by the ratio of the consistency index to the random consistency index. If the consistency ratio is less than 0.1, the matrix is ​​deemed to meet the rationality requirements. If it does not meet the requirements, the elements of the coupling judgment matrix are adjusted and the above calculation is repeated. Finally, the initial weight vector that passes the consistency check is normalized to obtain the contribution ratio of each evaluation index to the thermal stability evaluation of lithium battery recycled separator samples. The sum of the contribution ratios of all indicators is 1.

[0051] It should be noted that the coupling judgment matrix in this application represents the quantitative logic of the coupling relationship between various evaluation indicators when evaluating the thermal stability of lithium battery recycled separator samples; the contribution ratio represents the relative importance of the evaluation indicator in the overall thermal stability evaluation and the proportion of its quantitative contribution share, which can be used to judge the importance of the evaluation indicator in the overall thermal stability evaluation.

[0052] In addition, in specific implementation, the corresponding contribution proportions are corrected according to the correction coefficients of all contribution proportions. The corrected contribution proportions can be obtained in the following way: First, the original contribution proportions of each evaluation indicator are precisely matched with their corresponding correction coefficients to ensure that the original contribution proportion of each indicator corresponds to its exclusive correction coefficient without mismatch or omission. Then, for each evaluation indicator, its original contribution proportion and the matched correction coefficient are numerically calculated to obtain the preliminary corrected contribution proportion of each evaluation indicator. Subsequently, the preliminary corrected contribution proportions of all evaluation indicators are summed to obtain the total value of the preliminary corrected contribution proportions of all indicators. Then, for each evaluation indicator, its preliminary corrected contribution proportion is divided by the total value to complete the normalization calibration process. After calibration, it is ensured that the sum of the corrected contribution proportions of all evaluation indicators is 1, maintaining the integrity and rationality of the weight system, and finally obtaining the corrected contribution proportion of each evaluation indicator.

[0053] In addition, in specific implementation, the preliminary thermal stability index of the lithium battery recycled separator sample can be obtained by fusing and analyzing all valid data sequences and the temperature deviation based on the corrected contribution ratios of all valid data sequences. This can be achieved in the following way: Normalize all valid data sequences by minimum-maximum value to ensure that the normalized data are all in the 0-1 range, where 1 corresponds to optimal performance and 0 corresponds to failure. Based on the corrected contribution ratios of each index, multiply each normalized valid data sequence by its corresponding corrected contribution ratio and sum them up. The accumulated value is used as the preliminary fusion value, and corrections are made based on the temperature deviation: if the average deviation is ≤0.5℃, the ambient temperature is stable, and the preliminary fusion value remains unchanged; if 0.5℃ < average deviation ≤1℃, the normalized value of the temperature-sensitive index is adjusted by a correction coefficient k = 1 - average deviation / 5 before being added; if the average deviation > If the temperature drops below 1°C, the current test is immediately terminated. The high and low temperature environment test chamber is then recalibrated, and the parallel sample is replaced to conduct the test again. The corrected preliminary fusion value is used as the preliminary thermal stability index of the lithium battery recycled separator sample. Other methods can be used for analysis in other embodiments, which are not limited here.

[0054] Furthermore, in specific implementation, the preliminary thermal stability index is corrected according to the aforementioned time-series correlation law. The thermal stability index of the lithium battery recycled separator sample can be obtained in the following way: In the time dimension, based on the time lag logic between the thermal response index and the morphological stability index in the time-series correlation law, if the thermal response index shows a significant change at a certain time point, i.e., the temperature change rate exceeds the preset heating rate 2... If a change is significant and the morphological stability index becomes abnormal after the preset lag time of the coupling relationship (i.e., the normalized value drops below 0.7), then the normalized value of the morphological stability index at that node is reduced proportionally according to the degree of abnormality. Simultaneously, the contribution ratio of the corresponding corrected morphological stability index is temporarily reduced by 5%. This ratio is derived from the quantitative analysis of the thermal failure mechanism of lithium battery recycled separators. The failure of recycled separators under thermal action follows a progressive law of "abnormal thermal response - morphological deformation - mechanical property degradation." When the thermal response index changes significantly, the morphological stability index becomes the core intermediate link in the development of thermal failure, and its degree of abnormality directly determines the thermal stability risk level. Through coupling verification of multiple batches of recycled separator samples, this adjustment ratio can accurately reflect the mutual influence between indicators under the time-series correlation law, without being excessive. The evaluation weight of a single indicator is amplified and then re-introduced into the fusion calculation logic of the preliminary thermal stability index to update the preliminary thermal stability index. The preset lag time is determined based on the statistical analysis of historical aligned monitoring data of lithium battery recycled separator thermal stability test. First, a full statistical analysis is performed on the test data of lithium battery recycled separator samples with different heating rates and different decommissioning conditions. The inflection point time difference between the thermal response index and the morphological stability index, and between the morphological stability index and the mechanical retention index is extracted, and the median of the time difference within the 95% confidence interval is taken as the basic lag time. In the spatial dimension, based on the spatial correlation law between local ambient temperature and the morphological performance of the corresponding point in the temporal correlation law, if the ambient temperature of a certain monitoring point is more than 5°C higher than the average ambient temperature, and the normalized value of the thermal shrinkage rate of the corresponding point is 0.For values ​​of 2 or higher, the weight of the morphological performance index at that location is increased to 30% of the overall morphological index. This proportion is determined based on the analysis of the local failure characteristics of lithium battery separators and the information contribution rate of spatial location data. Thermal failure of lithium battery separators often begins with localized morphological deformation caused by localized high temperatures, gradually spreading to the whole. Correlation analysis of localized high-temperature locations and overall separator thermal failure shows that the morphological performance data at such locations contributes approximately 30% to the overall thermal stability risk. Increasing its weight to 30% highlights the impact of localized failure risk on the overall evaluation while retaining 70% of the weight for other normal locations, achieving a comprehensive consideration of local anomalies and overall performance, consistent with the thermal failure patterns of separators in practical applications. The preliminary thermal stability index calculation results are then adjusted again. The final preliminary thermal stability index, corrected by the time-series correlation law, is used as the thermal stability index of the lithium battery separator sample. Other correction methods can be used in other embodiments, which are not limited here.

[0055] It should be noted that the preliminary thermal stability index in this application represents the initial basic comprehensive index of the thermal stability of the lithium battery recycled separator sample. It reflects the initial thermal stability level of the lithium battery recycled separator after eliminating the interference of temperature fluctuations, according to the importance weight of each evaluation index. The thermal stability index represents the stability test index of the thermal stability of the lithium battery recycled separator sample. It reflects the final quantitative result of the comprehensive thermal stability performance of the separator. It integrates the differences in importance of each index and the influence of temperature control accuracy, and also reflects the spatiotemporal interaction between the indexes. The score directly corresponds to the high-temperature safety and reliability level of the separator in actual application, providing a direct basis for sample performance evaluation, batch comparison and process optimization.

[0056] In another aspect, in some embodiments, this application provides a lithium battery recycling separator thermal stability testing system, referring to... Figure 4 The figure is a schematic diagram of a lithium battery recycling membrane thermal stability testing system according to some embodiments of this application. The lithium battery recycling membrane thermal stability testing system 400 includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to acquire the real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycling separator sample in the testing system, so as to form multi-dimensional test data of the lithium battery recycling separator sample. Processing module 402, in this application, is used to extract the effective data sequence of each evaluation index from the multi-dimensional test data for each evaluation index of thermal stability evaluation based on the lithium battery recycled separator sample. It should be noted that the processing module 402 in this application is also used to determine the discrete characteristics of the effective data sequence corresponding to each evaluation index, and to determine the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling separator based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling separator sample. In addition, it should be noted that the processing module 402 in this application is also used to classify the various evaluation indicators into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and to determine the temporal correlation law of the three types of indicators under thermal action. The execution module 403 in this application is mainly used to collect the ambient temperature data of the test scenario in which the lithium battery recycling membrane sample is located through an ambient temperature sensor, and to perform coupled analysis on the thermal stability of the lithium battery recycling membrane sample based on the correction coefficient of all contribution proportions, the time-series correlation law and the ambient temperature data throughout the test scenario, so as to obtain the thermal stability index of the lithium battery recycling membrane sample.

[0057] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described method for testing the thermal stability of lithium battery recycling membranes.

[0058] In some embodiments, reference Figure 5 The figure is a schematic diagram of a computer device for implementing a method for testing the thermal stability of lithium battery recycling separators according to some embodiments of this application. The lithium battery recycling separator thermal stability testing method in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0059] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0060] The communication bus 502 can be used to transmit information between the aforementioned components.

[0061] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0062] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0063] Communication interface 504 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0064] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0065] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0066] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for testing the thermal stability of lithium battery recycling separators.

[0067] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0068] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for testing the thermal stability of a lithium battery recycling separator, wherein, The method involves pre-processing lithium battery recycled separator samples and setting up a standardized testing environment. This testing environment includes a high and low temperature environmental test chamber, a universal testing machine, a non-contact laser length measuring instrument, and an ambient temperature sensor. After calibrating the testing system corresponding to the testing environment, thermal stability testing is conducted. The method is characterized by the following steps: The real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycled separator sample in the testing system are obtained to form multi-dimensional test data of the lithium battery recycled separator sample. The evaluation indicators for thermal stability evaluation based on the lithium battery recycled separator sample are extracted from the multi-dimensional test data to obtain the effective data sequence of each evaluation indicator. The discrete characteristics of the effective data sequence corresponding to each evaluation index are determined. Based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling membrane, the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling membrane sample is determined. The evaluation indicators are divided into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and the temporal correlation law of the three types of indicators under thermal action is determined. The ambient temperature data of the lithium battery recycled separator sample in the test environment is collected by an ambient temperature sensor. The thermal stability of the lithium battery recycled separator sample is coupled and analyzed based on the correction coefficient of all contribution proportions, the time-series correlation law and the ambient temperature data throughout the test environment to obtain the thermal stability index of the lithium battery recycled separator sample.

2. The method as described in claim 1, characterized in that, The evaluation indicators for thermal stability assessment based on the recycled lithium battery separator samples are extracted from the multi-dimensional test data, specifically including: The various evaluation indicators for thermal stability evaluation of the lithium battery recycled separator sample were obtained. Anomaly processing is performed on the multi-dimensional test data to obtain anomaly-processed multi-dimensional test data; The effective data sequences of each evaluation index are extracted from the multi-dimensional test data after the anomaly handling.

3. The method as described in claim 1, characterized in that, Determining the discrete characteristics of the effective data sequences corresponding to each evaluation index specifically includes: Calculate the standard deviation, coefficient of variation, and range of the effective data series corresponding to each evaluation index; Each standard deviation, coefficient of variation, and range is used as a discrete feature of the corresponding valid data sequence.

4. The method as described in claim 1, characterized in that, Based on the influence characteristics of each discrete feature and each evaluation index on the lithium battery recycling separator, the correction coefficients for the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling separator sample are determined, specifically including: Based on the thermal failure mechanism and engineering application core of lithium battery recycling membrane, the influence characteristics of each evaluation index on the lithium battery recycling membrane are determined. By coupling the discrete features and all the influencing features, the initial correction coefficients of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample are obtained. All initial correction coefficients are calibrated to obtain the correction coefficients for the contribution of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample.

5. The method as described in claim 1, characterized in that, Determining the temporal correlation patterns of the three types of indicators under thermal action specifically includes: Obtain historical monitoring data corresponding to the three types of indicators; All historical monitoring data are time-aligned to obtain historical aligned monitoring data; The temporal correlation patterns of the three types of indicators under thermal action were determined using the historical alignment monitoring data.

6. The method as described in claim 1, characterized in that, A coupled analysis of the thermal stability of the lithium battery recycled separator sample was performed based on the correction coefficients for all contribution percentages, the time-series correlation patterns, and the overall ambient temperature data. The resulting thermal stability index of the lithium battery recycled separator sample specifically includes: Obtain the preset temperature rise curve of the lithium battery recycled separator sample; Determine the temperature deviation between the ambient temperature data throughout the process and the preset temperature rise curve; Determine the contribution percentage of each evaluation index to the thermal stability evaluation of the lithium battery recycled separator sample. The corresponding contribution percentages are adjusted based on the adjustment coefficients of all contribution percentages to obtain the adjusted contribution percentages. Based on the contribution percentages of all corrected data sequences and the temperature deviations, a preliminary thermal stability index of the lithium battery recycled separator sample is obtained by performing a fusion analysis. The preliminary thermal stability index is corrected based on the time-series correlation law to obtain the thermal stability index of the lithium battery recycled separator sample.

7. The method as described in claim 1, characterized in that, The multi-dimensional test data includes real-time temperature data at different monitoring points on the sample surface, longitudinal and transverse tensile strength data, and length and width dimensional data.

8. A thermal stability testing system for lithium battery recycling separators, wherein, The pretreatment of lithium battery recycled separator samples and the establishment of a standardized testing environment are completed in advance. This testing environment includes a high and low temperature environmental test chamber, a universal testing machine, a non-contact laser length measuring instrument, and an ambient temperature sensor. After calibrating the testing system corresponding to the testing environment, thermal stability testing is conducted. The system is characterized by comprising: The acquisition module is used to acquire real-time ambient temperature, real-time tensile force and strain, and real-time dimensional deformation data of the lithium battery recycled separator sample in the testing system, forming multi-dimensional test data of the lithium battery recycled separator sample; The processing module is used to extract the effective data sequence of each evaluation index from the multi-dimensional test data for evaluating the thermal stability of the lithium battery recycled separator sample. The processing module is also used to determine the discrete characteristics of the effective data sequence corresponding to each evaluation index, and to determine the correction coefficient of the contribution ratio of each evaluation index to the thermal stability evaluation of the lithium battery recycling separator sample based on the discrete characteristics and the influence characteristics of each evaluation index on the lithium battery recycling separator. The processing module is also used to classify the various evaluation indicators into thermal response indicators, mechanical retention indicators, and morphological stability indicators, and to determine the temporal correlation law of the three types of indicators under thermal action. The execution module is used to collect the ambient temperature data of the test scenario in which the lithium battery recycled separator sample is located through an ambient temperature sensor, and to perform coupled analysis on the thermal stability of the lithium battery recycled separator sample based on the correction coefficients of all contribution proportions, the time-series correlation law, and the ambient temperature data throughout the test scenario, so as to obtain the thermal stability index of the lithium battery recycled separator sample.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the lithium battery recycling membrane thermal stability test method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for testing the thermal stability of lithium battery recycling separators as described in any one of claims 1 to 7.

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