A method for predicting the life of a plastic on a battery cell

Abstract

The application relates to the technical field of battery testing, and discloses a plastic-on-cell life prediction method, which comprises the following steps: S1, preparing a top cover sample containing plastic-on-cell and a gasket fixed on a pole; S2, performing an accelerated temperature and humidity aging test and a gasket pull-out force test on the top cover sample, fitting accelerated aging model parameters according to the results, and calculating equivalent accelerated aging time; S3, performing high-temperature and high-humidity accelerated aging on the top cover sample within the equivalent accelerated aging time to obtain an aging sample; S4, simulating the expansion force of the battery cell, obtaining a load spectrum, and outputting the corresponding gasket pull-out force and cycle number of each section through module simulation; and S5, performing a fatigue pull-out test on the aging top cover sample according to the gasket pull-out force and cycle number of each section, judging whether the plastic-on-cell is cracked, and evaluating the cracking life. The method realizes coupling evaluation of temperature and humidity aging and cyclic expansion force load, and solves the problem that single-dimensional evaluation in the prior art cannot accurately reflect the cracking risk of the plastic-on-cell in a real service environment.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method for predicting the lifespan of plastic on a battery cell. Background Technology

[0002] The top plastic layer of a lithium-ion battery is a critical insulation and structural support component installed between the top cover and the terminals, enduring harsh multiple service loads throughout the battery's lifespan. On one hand, the battery's internal environment is constantly exposed to high temperature and humidity, causing irreversible temperature and humidity aging of the top plastic material, manifested as a decrease in elastic modulus, loss of toughness, and reduction in tensile strength, resulting in deterioration of mechanical properties. On the other hand, during repeated charging and discharging, the cell experiences periodic volume expansion and contraction due to the lithium insertion / extraction behavior of the electrode active particles. This cyclic expansion force is transmitted to the top plastic layer via the terminals and top cover, subjecting it to alternating mechanical tensile loads. Under the combined effect of these two factors, the top plastic layer is highly susceptible to crack initiation and gradual propagation in stress concentration areas, ultimately leading to cracking failure and causing serious safety issues such as insulation failure, short circuits, and even thermal runaway.

[0003] However, existing methods for assessing the reliability of top plastics have significant limitations. Material suppliers or battery manufacturers typically select materials based solely on standard performance data of raw materials (such as tensile strength and elongation at break), or measure residual mechanical properties after conducting high-temperature and high-humidity aging tests to determine material suitability. These methods completely ignore the continuous mechanical load input of cell cyclic expansion force, failing to reflect the cracking risk of materials under actual stress conditions after aging. On the other hand, while some studies involve simulations or measurements of cell expansion force, their endpoint is limited to obtaining expansion force values ​​or curves, without further examining the impact of this mechanical load on the structural integrity of the top plastic. Therefore, existing methods struggle to accurately reflect the cracking risk of the top plastic under real-world battery usage conditions. Summary of the Invention

[0004] In view of this, this application proposes a method for predicting the lifespan of plastic on the battery cell, in order to solve the technical problem that the existing technology is not accurate enough in assessing the cracking risk of the plastic on the battery cell under service conditions.

[0005] The technical solution of this application is implemented as follows: This application provides a method for predicting the lifespan of plastic on a battery cell, including the following steps: S1. Prepare a top cover sample including the upper plastic and the electrode post with the bar plate fixed on it; S2. Accelerated temperature and humidity aging tests were conducted on multiple groups of top cover samples under different temperature and humidity conditions. Pull-out force tests were conducted on each group of aged samples. The activation energy and humidity coefficient of the accelerated aging model were fitted based on the pull-out force test results. The activation energy and humidity coefficient were substituted into the accelerated aging model and the equivalent accelerated aging time was calculated in combination with the target service environment. S3. The top cover sample is subjected to accelerated temperature and humidity aging in a high temperature and high humidity environment within the equivalent accelerated aging time to obtain the aged top cover sample. S4. Perform expansion force simulation on the battery cell to obtain the total number of cycles and load spectrum of the battery cell under the preset life end condition. Substitute the load spectrum into the module for simulation according to the life cycle sequence, and output the minimum and maximum pull-out force values ​​and the number of cycles corresponding to each segment. S5. After aging, the top cover sample is subjected to fatigue pull-out test according to the minimum and maximum pull-out force values ​​and the number of cycles for each section. The results of the fatigue pull-out test are used to determine whether the plastic on the cell has cracked, so as to evaluate the crack life of the plastic on the cell under service environment.

[0006] In some embodiments, accelerated temperature and humidity aging tests are conducted on multiple groups of top cover samples under different temperature and humidity conditions, and a pull-out force test is performed on each group of aged samples. The activation energy and humidity coefficient of the accelerated aging model are fitted based on the pull-out force test results, specifically including: Before conducting accelerated temperature and humidity aging tests, the initial pull-out force of the top plate of the unaged top cover sample was obtained. After the accelerated temperature and humidity aging test, the top cover sample under each temperature and humidity condition was sampled and tested for the pull-out force of the sheet in multiple time batches. The pull-out force retention rate corresponding to each time batch was calculated. The pull-out force retention rate is the ratio of the pull-out force after aging to the initial pull-out force. The relationship between the pull-out force retention rate and aging time is fitted, and the critical aging time when the pull-out force retention rate drops to a preset threshold is obtained by interpolation. The activation energy and humidity coefficient are obtained by linear fitting based on the critical aging time, temperature value, and humidity value corresponding to multiple temperature and humidity conditions.

[0007] In some embodiments, step S2, which involves calculating the equivalent accelerated aging time in conjunction with the target service environment, specifically includes: Obtain the average temperature, average humidity, and preset service life of the target service environment; Temperature values ​​below the glass transition temperature of the upper plastic and high humidity values ​​are selected as accelerated aging test conditions. The activation energy, humidity coefficient, target service environment parameters, and accelerated aging test conditions are substituted into the accelerated aging model to calculate the equivalent accelerated aging time.

[0008] In some embodiments, the preset threshold is 50% of the initial pull-out force; the accelerated aging model is the Peck model, and the activation energy and humidity coefficient are solved by a binary linear fitting method.

[0009] In some embodiments, the specific steps in step S4, which involve obtaining the total number of cycles and load spectrum of the battery cell under preset end-of-life conditions, segmenting the load spectrum according to the life cycle sequence, inputting it into the module for simulation, and outputting the minimum and maximum pull-out force values ​​of the battery corresponding to each segment, include: Using the preset percentage of cell capacity decay to rated capacity as the preset lifespan termination condition, a cell charge-discharge cycle expansion force simulation was performed to obtain the total number of cycles and the curves of the minimum and maximum expansion forces per cycle as a function of the number of cycles. The change curve is divided into several time segments of equal length according to the life cycle sequence, and each time segment corresponds to the same number of cycles. Extract the minimum and maximum expansion force extreme values ​​corresponding to each time segment, substitute them into the module structure simulation model, and obtain the minimum and maximum pull-out force values ​​of the blister pack corresponding to each time segment.

[0010] In some embodiments, obtaining the minimum and maximum pull-out force values ​​of the plaster corresponding to each time segment specifically includes: A thermo-coupling method was used to establish a cell material expansion model. By setting the thermal expansion coefficient and temperature variable, the expansion force change of the cell in the thickness direction was simulated, and the large-area expansion force data of the cell was output. Align the cell surface expansion force data with the corresponding maximum and minimum expansion force values ​​for that segment, determine the corresponding time step, and output the force on each plate at that time step. Subtract the force on each piece of plaster at the maximum expansion force value from the force at the minimum expansion force value to obtain the force difference of each piece of plaster. Take the piece of plaster with the largest force difference, and take the force on that piece of plaster at the maximum expansion force value as the maximum pull-out force value corresponding to that segment, and take the force on that piece of plaster at the minimum expansion force value as the minimum pull-out force value corresponding to that segment.

[0011] In some embodiments, in step S4, the preset lifespan termination condition is that the cell capacity decays to 80% of the rated capacity.

[0012] In some embodiments, step S5 involves sequentially performing fatigue pull-out tests on the aged top cover sample according to the minimum and maximum pull-out force values ​​and the number of cycles applied to each segment of the sheet. Specifically, this includes: The aged top cover sample is clamped in a fatigue testing machine, and the minimum and maximum pull-out force values ​​corresponding to each segment are applied sequentially according to the life cycle sequence. The corresponding number of cycles is applied to each segment until the loading of all segments is completed. During the loading process, the mechanical response of the connection area between the pad and the upper plastic is monitored in real time, and the segment and cycle number corresponding to the abnormality are recorded.

[0013] In some embodiments, step S5, which determines whether the upper plastic has cracked based on the fatigue pull-out test results, includes: acquiring the pull-out force-displacement curve in real time during the fatigue pull-out test; and determining that the upper plastic has cracked when the pull-out force-displacement curve shows a sudden change in slope or a sudden drop in force.

[0014] In some embodiments, step S5, evaluating the crack life of the plastic on the cell under service conditions, includes: comparing the number of cycles corresponding to the occurrence of cracking with the total number of cycles; if the number of cycles at the occurrence of cracking is greater than or equal to the total number of cycles, the plastic on the cell is determined to meet the life requirement; otherwise, the plastic on the cell is determined not to meet the life requirement.

[0015] This application has the following advantages over the prior art: (1) The method disclosed in this application prepares a top cover sample containing the upper plastic and the plate connection structure, obtains the material aging parameters and mechanical load spectrum by using accelerated temperature and humidity aging test and expansion force simulation respectively, and then conducts fatigue pull-out test on the aged sample according to the load spectrum. This realizes the coupled evaluation of temperature and humidity aging and cyclic expansion force load, solves the problem that the single-dimensional evaluation in the prior art cannot accurately reflect the cracking risk of the upper plastic in the real service environment, and improves the accuracy of life prediction and engineering practicality.

[0016] (2) By obtaining the initial pull-out force, testing the pull-out force retention rate in batches, interpolating to obtain the critical aging time, and linear fitting parameters, a complete method for converting raw experimental data into model parameters was established. This method uses the pull-out force of the plastic sheet, a mechanical index directly related to the actual failure mode of the plastic sheet, as the aging characterization quantity, avoiding the uncertainty brought about by using indirect material performance indicators, and improving the fitting accuracy and engineering applicability of the accelerated aging model parameters.

[0017] (3) By obtaining the target service environment parameters, selecting reasonable accelerated test conditions, substituting them into the accelerated aging model to calculate the acceleration factor and converting it into equivalent aging time, a quantitative mapping path from the actual service environment to the laboratory accelerated conditions was established. This method ensures that the accelerated aging test can significantly shorten the test cycle while keeping the failure mechanism unchanged, and provides a reliable theoretical basis and engineering parameters for obtaining aging samples equivalent to the actual service state.

[0018] (4) By simulating the expansion force with the cell capacity decaying to a preset ratio as the end-of-life condition, the continuously changing expansion force curve is divided into segments of equal time according to the life cycle sequence and the extreme values ​​are extracted. Then, the extreme values ​​are substituted into the module simulation to obtain the pull-out force of the plastic sheet, realizing the step-by-step transformation from the cell expansion behavior to the actual stress on the plastic sheet. Among them, the segmentation strategy simplifies the complex fatigue process of thousands of turns into a limited number of constant amplitude load blocks. Under the premise of ensuring the load envelope and representativeness, it significantly reduces the amount of simulation calculation and the difficulty of test execution, and significantly improves the evaluation efficiency.

[0019] (5) By applying load spectrums sequentially to the aged top cover specimen according to its life cycle and conducting fatigue pull-out tests, and monitoring the mechanical response in real time during the loading process, the physical coupling verification of temperature and humidity aging effect and cyclic mechanical load was achieved. This method completes the cumulative loading of the full life cycle load spectrum with a single specimen, which significantly shortens the test cycle while ensuring damage equivalence. At the same time, by recording abnormal moments, it achieves an accurate quantitative assessment of the life of the plastic, solving the problem that aging test and mechanical test are separated in the prior art and cannot reflect the coupled failure mechanism. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the method for predicting the lifespan of plastic on a battery cell disclosed in this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] This application can be applied to scenarios where reliability assessment and lifespan prediction of plastic components are performed during the design and development phase of various lithium-ion power batteries. In particular, for the plastic components of prismatic cells that need to withstand the coupled effects of temperature and humidity aging and charge-discharge cycle expansion forces throughout the cell's entire lifespan, this application provides a method that organically combines accelerated material aging experiments with cell expansion force simulation, and accurately evaluates its crack life through fatigue tests with equivalent load spectrum transformation.

[0024] like Figure 1 As shown in the figure, this application discloses a method for predicting the lifespan of plastic on a battery cell, including the following steps: Step S1: Prepare a top cover sample containing plastic and a plate fixed on the pole.

[0025] Specifically, the upper plastic, electrode post, and switch plate used in actual production are taken. The switch plate is welded and fixed to the electrode post, and then the electrode post is assembled into the upper plastic to form a complete top cover assembly sample. This sample retains the true geometric structure and stress concentration characteristics of the connection area between the switch plate and the upper plastic, ensuring that subsequent test results can reflect the failure behavior under actual working conditions.

[0026] Step S2: Accelerated temperature and humidity aging tests are conducted on multiple groups of top cover samples under different temperature and humidity conditions. After aging, a pull-out force test is conducted on each group of samples. The activation energy and humidity coefficient of the accelerated aging model are fitted based on the pull-out force test results. The activation energy and humidity coefficient are substituted into the accelerated aging model and combined with the target service environment to calculate the equivalent accelerated aging time.

[0027] This step involves obtaining the degradation law of material properties with temperature and humidity changes through accelerated aging tests, and establishing a mapping relationship from laboratory accelerated conditions to actual service environments. Setting multiple sets of different temperature and humidity conditions can cover the range of environmental stresses that the battery may experience, making the fitted activation energy and humidity coefficient more universal. The pull-out force test directly measures the mechanical properties of the interface between the upper plastic and the plastic sheet; the degradation of these properties directly reflects the degree of deterioration of the upper plastic in a humid and hot environment. The activation energy characterizes the sensitivity of temperature to the aging rate, and the humidity coefficient characterizes the weight of humidity's influence on the aging rate; together, they determine the acceleration factor of the accelerated aging model. Substituting these two parameters into the model and combining them with the temperature, humidity, and design life of the target service environment, the required aging time under laboratory conditions can be calculated. This aging time is equivalent to the total temperature and humidity aging effects experienced by the upper plastic during actual battery use, thus providing samples with consistent aging levels for subsequent steps.

[0028] Step S3: The top cover sample is subjected to accelerated temperature and humidity aging in a high temperature and high humidity environment within the equivalent accelerated aging time to obtain the aged top cover sample.

[0029] This step involves performing actual accelerated aging. The top cover sample is placed in a high-temperature and high-humidity test chamber and aged according to the equivalent accelerated aging time calculated in S2, so that the sample reaches the same aging state as throughout the battery's entire life cycle. The high-temperature and high-humidity environment accelerates the molecular chain degradation and hydrolysis of the plastic material, leading to a gradual decrease in its mechanical properties such as elastic modulus, tensile strength, and toughness. The aged top cover sample represents the material state of the plastic material at the end of the battery cell's service life, providing a realistic aging background for subsequent coupled fatigue tests.

[0030] Step S4: Perform expansion force simulation on the battery cell to obtain the total number of cycles and load spectrum of the battery cell under the preset end-of-life conditions. Substitute the load spectrum into the module for simulation in segments according to the life cycle sequence, and output the minimum and maximum pull-out force values ​​and the number of cycles for each segment.

[0031] The purpose of this step is to convert the periodic expansion forces generated by the battery cell during charging and discharging into a mechanical load spectrum that the plastic substrate actually bears. A load spectrum over the entire life cycle is obtained through cell-level simulation, reflecting the evolution of the cell's expansion behavior from the initial to the later stages. The load spectrum is segmented according to the life cycle sequence; this discretization simplifies the continuous and complex load history into an engineering-achievable loading spectrum. Subsequently, the load spectra of each segment are substituted into the module-level simulation, considering the force transmission and amplification effects of the module structure, ultimately outputting the minimum and maximum pull-out force values ​​and the number of cycles for each segment. Through this step, the originally abstract expansion force data is transformed into a pull-out force load spectrum that can be directly applied to the plastic substrate, providing accurate mechanical input for subsequent fatigue testing.

[0032] S5. After aging, the top cover sample is subjected to fatigue pull-out test according to the minimum and maximum pull-out force values ​​and the number of cycles for each section. The results of the fatigue pull-out test are used to determine whether the plastic on the cell has cracked, so as to evaluate the crack life of the plastic on the cell under service environment.

[0033] This step is the verification stage of the entire method, physically coupling the aged samples and load spectra obtained in the preceding steps. The top cover sample, which has undergone temperature and humidity aging, is clamped in a fatigue testing machine. The minimum and maximum pull-out forces corresponding to each segment are applied sequentially according to the battery life cycle, with a corresponding number of cycles applied to each segment. This simulates the combined effects of environmental aging and cyclic mechanical loads simultaneously on the upper plastic layer during actual battery use. Through fatigue pull-out tests, the failure behavior of the upper plastic layer under coupled loads is directly observed. Based on the test results, it is determined whether cracking has occurred in the upper plastic layer, thus assessing its crack life under service conditions. This method allows for accurate assessment of the reliability of the upper plastic layer in the battery cell during the product design stage, avoiding the drawbacks of traditional methods where aging testing and mechanical testing are separated.

[0034] Taking a specific application scenario as an example, this embodiment predicts and assesses the cracking risk of the plastic on a 120Ah square-shell lithium-ion power battery during its 10-year service life.

[0035] First, in step S1, multiple top cover samples are prepared, and conductive electrodes are fixed on the positive and negative electrodes of the samples by laser welding.

[0036] Next, in step S2, unaged top cover samples are selected for a bar pull-out force test to obtain the initial pull-out force F. Then, multiple sets of accelerated temperature and humidity aging tests are conducted on multiple sets of top cover samples under different temperature and humidity conditions, such as 85°C / 95%RH, 80°C / 95%RH, 75°C / 95%RH, 85°C / 85%RH, 80°C / 85%RH, and 75°C / 85%RH. The bar pull-out force of the aged samples is tested in multiple time batches, and the pull-out force retention rate is calculated. By fitting the relationship between the pull-out force retention rate and aging time, the critical aging time when the pull-out force retention rate drops to 50% of the initial pull-out force F under each temperature and humidity condition is interpolated to obtain the critical aging time. Based on the obtained multiple sets of critical aging times, temperature values, and humidity values, a binary linear fitting is performed using the Peck accelerated aging model to accurately solve for the activation energy of the plastic material. E a With humidity coefficient n .

[0037] Next, relevant parameters of the target service environment are obtained. For example, taking the relatively harsh environment of Sanya as a representative, the average cell temperature is set at 27°C, the average humidity at 79%RH, and the service life requirement at 10 years. Combined with test conditions lower than the glass transition temperature of the upper plastic and capable of accelerating testing (such as 85°C and 95%RH), the following will be determined. E a , n Substituting all parameters into the accelerated aging model, the equivalent accelerated aging time is calculated. t test According to step S3, a new batch of top cover samples are placed in a high-temperature and high-humidity test chamber and aged at 85°C and 95%RH for a specified period of time. t test A top cover sample was obtained after simulating 10 years of temperature and humidity aging.

[0038] On the other hand, step S4 is carried out in parallel. First, the cell expansion force simulation is performed, with the cell capacity decaying to 80% of the rated capacity as the preset lifespan termination condition. The total number of cycles N for the cell is obtained through simulation calculation, and the minimum and maximum expansion force variation curves in each cycle are obtained. The entire variation curve is divided into ten equal-length time segments according to the lifespan sequence, with each segment corresponding to N / 10 cycles. The extreme values ​​of the minimum and maximum expansion forces in each segment are extracted as representative loads for that segment. These ten sets of minimum and maximum expansion force values ​​are substituted into the structural simulation model of the module where the cell is located, and the minimum and maximum pull-out force values ​​acting on the plate corresponding to each segment are calculated.

[0039] Finally, in step S5, the aged top cover sample obtained in step S3 is clamped on a fatigue testing machine. According to the ten-segment load spectrum generated in step S4, the corresponding minimum and maximum pull-out force values ​​are applied sequentially from the first to the tenth segment, with each segment repeated N / 10 times. During the loading process, the connection area between the plastic and the metal plate on the sample is monitored in real time to check for crack initiation or breakage. If no cracks appear in the plastic after the entire ten-segment load spectrum has been applied, the plastic is deemed to meet the 10-year service life requirement, and the design scheme passes the evaluation.

[0040] This method prepares a top cover sample containing a plastic upper layer and a sheet-like connecting structure. It obtains material aging parameters and mechanical load spectra by using accelerated temperature and humidity aging tests and expansion force simulation, respectively. Then, it conducts fatigue pull-out tests on the aged sample according to the load spectrum. This method realizes the coupled evaluation of temperature and humidity aging and cyclic expansion force load, which solves the problem that the single-dimensional evaluation in the existing technology cannot accurately reflect the cracking risk of the plastic upper layer in the real service environment. This improves the accuracy of life prediction and engineering practicality.

[0041] In some embodiments, for step S2 above, fitting the activation energy and humidity coefficient of the accelerated aging model based on the pull-out force test results specifically includes: First, before conducting the accelerated temperature and humidity aging test, the initial pull-out force of the top cover sample was obtained.

[0042] This step is used to establish a baseline reference value for material properties. The pull-out force of the adhesive strip in its unaged state represents the mechanical strength of the interface between the upper plastic and the adhesive strip in its new state, and serves as the fundamental zero point for subsequent measurement of aging. By measuring the initial pull-out force, samples from different batches and processes can be normalized to the same baseline, eliminating the influence of individual differences on aging assessment and ensuring the comparability of subsequently calculated aging degradation ratios.

[0043] Subsequently, after the accelerated temperature and humidity aging test, the top cover sample under each temperature and humidity condition was sampled and tested for the pull-out force of the sheet in multiple time batches. The pull-out force retention rate corresponding to each time batch was calculated. The pull-out force retention rate is the ratio of the pull-out force after aging to the initial pull-out force.

[0044] This step involves collecting mechanical property data at different aging time points to track the continuous degradation trajectory of material properties as the aging process progresses. The pull-out force retention rate visually reflects the percentage of remaining strength of the plastic after experiencing a specific temperature and humidity environment, converting absolute force values ​​into relative proportions, allowing for comparison of samples with different initial strengths on the same scale. Sampling from multiple time batches captures the nonlinear characteristics of the degradation process, providing sufficient data points for subsequent interpolation.

[0045] Then, the relationship between the pull-out force retention rate and aging time is fitted, and the critical aging time when the pull-out force retention rate drops to a preset threshold is obtained by interpolation.

[0046] This step transforms discrete test data into a continuous mathematical model, thereby accurately determining the time required for the material to reach a specific failure criterion. By fitting the retention rate curve over time, interpolation is used to calculate the aging time corresponding to the retention rate just dropping to a preset threshold. This time is the critical aging time under that temperature and humidity condition. The critical aging time characterizes how quickly the material's performance degrades to an unacceptable level under that environmental stress and is a key input for subsequent fitting of the accelerated model parameters.

[0047] As a specific implementation method, the preset threshold can be set to 50% of the initial pull-out force. When the pull-out force of the plastic drops to half of the initial value, it usually means that the material has undergone significant mechanical property degradation. Using this as an aging benchmark has both physical significance and engineering safety.

[0048] Finally, based on the critical aging time, temperature value, and humidity value corresponding to multiple temperature and humidity conditions, linear fitting was performed to obtain the activation energy and humidity coefficient.

[0049] This step correlates the critical aging time under different environmental stresses with corresponding temperature and humidity data, and uses linear regression to fit two core parameters in the accelerated aging model. The activation energy reflects the material's sensitivity to temperature changes, and the humidity coefficient reflects its sensitivity to humidity changes; together, they determine the magnitude of the acceleration factor. In this way, scattered experimental data are transformed into physically meaningful model parameters, enabling the accelerated aging model to quantitatively describe the accelerating effect of temperature and humidity environments on material aging, providing reliable parameter basis for subsequent calculations of equivalent accelerated aging times.

[0050] By adopting the above technical solution, a complete method for converting raw experimental data into model parameters was established. This method involves obtaining the initial pull-out force, testing the pull-out force retention rate in batches, interpolating to obtain the critical aging time, and using linear fitting parameters. The method uses the pull-out force of the plastic sheet, a mechanical index directly related to the actual failure mode of the plastic, as the aging characterization quantity. This avoids the uncertainty caused by using indirect material performance indicators and improves the fitting accuracy and engineering applicability of the accelerated aging model parameters.

[0051] In some embodiments, the calculation of the equivalent accelerated aging time in step S2 in conjunction with the target service environment specifically includes the following schemes.

[0052] First, the average temperature, average humidity, and preset service life of the target service environment are obtained. The target service environment refers to the temperature and humidity conditions experienced by the battery during actual use, typically represented by the annual average temperature and humidity of the vehicle's operating area. The preset service life is the total service time required by the battery design, such as the vehicle warranty period or the design life of the battery pack. These three parameters constitute the environmental stress input on the actual use side in the accelerated aging model and serve as the benchmark for calculating the acceleration factor.

[0053] Secondly, temperatures below the glass transition temperature (GLT) of the plastic and high humidity levels were selected as accelerated aging test conditions. The glass transition temperature is the critical temperature at which the plastic material transitions from a glassy state to a highly elastic state. Exceeding this temperature causes drastic changes in the material's mechanical properties and alters the aging mechanism. Therefore, the accelerated test temperature must be below the GLT to ensure that the aging mechanism is consistent with the actual service environment. High humidity is used to accelerate the corrosive effect of moisture on the material; typically, 95% RH or near-saturation relative humidity is chosen. Substituting the activation energy, humidity coefficient, target service environment parameters, and accelerated test conditions into the accelerated aging model, the acceleration factor can be calculated. The Hallberg-Peck model is used for accelerated aging, and its acceleration factor expression is as follows:

[0054] Wherein, AF is the acceleration factor, which is dimensionless; and These are the relative humidity and absolute temperature for the accelerated aging test, respectively. and These represent the relative humidity and absolute temperature of the target service environment, respectively. The activation energy is expressed in electron volts or joules per mole. n Humidity coefficient, dimensionless. k Let be the Boltzmann constant, taken as 8.617 × 10⁻⁶. -5eV / K. This formula quantitatively describes the combined accelerating effect of temperature and humidity on the aging rate: the temperature term follows an Arrhenius relation, the humidity term follows a power law relation, and multiplying the two yields the total acceleration factor. Substituting the acceleration factor and the preset service life into the following formula, the equivalent accelerated aging time can be obtained: .

[0055] in, To effectively accelerate aging time, L This represents the preset service life. The equivalent accelerated aging time indicates how long it takes to age under selected accelerated test conditions to be equivalent to all the aging damage a battery experiences in a real-world service environment. This calculation compresses the actual service aging process, which can take years or even decades, into a short-cycle accelerated test that can be performed in the laboratory, while ensuring the consistency of the aging mechanism and the equivalence of the degree of damage.

[0056] By acquiring target service environment parameters, selecting reasonable accelerated test conditions, substituting them into an accelerated aging model to calculate the acceleration factor, and converting the equivalent aging time, a quantitative mapping path from the actual service environment to laboratory accelerated conditions was established. This method ensures that accelerated aging tests can significantly shorten the test cycle while maintaining the failure mechanism unchanged, providing a reliable theoretical basis and engineering parameters for obtaining aging samples equivalent to real service conditions.

[0057] In some embodiments, the accelerated aging model is selected from the Peck model, which is widely used in the semiconductor and electronic packaging fields. The parameters are solved by transforming the model into a linear equation by taking the logarithm of both sides. Taking the natural logarithm of both sides of the Peck model, we obtain... .

[0058] in, This is the critical aging time. C For the combined constant term, As the dependent variable, with and Using the critical aging time, temperature, and humidity values ​​under multiple temperature and humidity conditions as the independent variable, a binary linear fit can be performed to intuitively and conveniently solve for the activation energy. E a With humidity coefficient n This method offers a clear data processing procedure and robust results.

[0059] As a specific implementation method, when selecting accelerated aging test conditions, it is necessary to ensure that the selected temperature value does not exceed the glass transition temperature of the plastic material (for example, for PPS reinforced with glass fiber, its glass transition temperature is approximately 93°C, and 85°C can be selected) to avoid changes in the physical state of the material under non-use conditions (such as the transition from a glassy state to a highly elastic state), thereby ensuring the consistency of the failure mechanism between accelerated aging and natural aging. The selection of a high humidity value (such as 95%RH) aims to maximize humidity-accelerated stress.

[0060] In one embodiment, in step S4, the total number of cycles and load spectrum of the battery cell under the preset end-of-life conditions are obtained, and the load spectrum is segmented according to the life cycle sequence and input into the module for simulation. The specific steps for outputting the minimum and maximum pull-out force values ​​of the corresponding segments are as follows.

[0061] First, using a preset percentage of cell capacity decay to rated capacity as the preset lifespan termination condition, cell charge-discharge cycle expansion force simulation was performed to obtain the total number of cycles and the curves showing the changes in minimum and maximum expansion force per cycle as a function of the number of cycles. During repeated charge-discharge processes, the active particles in the electrodes undergo lithium insertion / extraction behavior, resulting in periodic volume expansion and contraction in the thickness direction of the cell. This expansion force evolves continuously with the number of cycles. Using a preset percentage of capacity decay to rated capacity as the lifespan termination condition, for example, when the capacity decays to 80%, the cell is considered to have reached the end of its lifespan; the corresponding number of cycles is the total number of cycles. Through cell-level simulation, the minimum and maximum expansion forces corresponding to each cycle can be obtained. The changes in these forces with the number of cycles reflect the complete evolution process of the cell's expansion behavior from the initial to the later stages, providing raw data for the subsequent construction of the load spectrum.

[0062] Secondly, the variation curve is divided into several equal-length time segments according to the life cycle sequence, with each time segment corresponding to the same number of cycles. The expansion force of the battery cell throughout its entire life cycle is not a constant value, but rather changes non-linearly with the number of cycles, increasing rapidly in the initial stage, stabilizing in the middle stage, and potentially decaying or abruptly changing in the later stage. This scheme transforms a complex fatigue process with thousands of cycles and slowly and continuously changing load magnitude into a combination of a limited number of constant amplitude load blocks, which constitutes a crucial step in the transformation from simulation to experiment. By segmenting the variation curve and extracting the extreme values ​​within each segment, the most critical load boundaries throughout the entire life cycle can be captured, namely the maximum stress range and minimum preload state experienced by the upper plastic at each stage, ensuring the envelope and representativeness of the load in subsequent fatigue tests. At the same time, this segmented processing avoids the huge computational cost of simulating and testing different loads for each cycle, simplifying the complex task that originally required processing thousands of sets of data into processing only a few representative load blocks, significantly improving evaluation efficiency.

[0063] Finally, the minimum and maximum expansion force extreme values ​​corresponding to each time segment are extracted and substituted into the module structure simulation model to obtain the minimum and maximum pull-out force values ​​of the pads for each time segment. The expansion force generated by the battery cell does not act directly on the upper plastic, but is transmitted to the module frame through the large surface of the battery cell, and then to the upper plastic via the terminals and pads. Therefore, it is necessary to substitute the expansion force data at the battery cell level into the module-level structural simulation model, considering factors such as the stiffness of the module frame, preload, and the interaction of multiple battery cells, to calculate the actual pull-out force at the pad position. Each time segment corresponds to a set of minimum and maximum expansion force extreme values. After substituting these values ​​into the module simulation, the minimum and maximum pull-out force values ​​of the pads corresponding to that segment are output, thus establishing a complete force transmission chain mapping relationship from the battery cell expansion force to the pad pull-out force.

[0064] By simulating the expansion force using the cell capacity decay to a preset percentage as the end-of-life condition, the continuously changing expansion force curve is divided into segments of equal time duration according to the life cycle sequence, and extreme values ​​are extracted. These extreme values ​​are then substituted into the module simulation to obtain the pull-out force of the plastic sheet, realizing a step-by-step transformation from the cell expansion behavior to the actual stress on the plastic sheet. The segmented strategy simplifies the complex fatigue process of thousands of revolutions into a finite number of constant amplitude load blocks. While ensuring the load envelope and representativeness, this significantly reduces the simulation computation and experimental execution difficulty, thus significantly improving evaluation efficiency.

[0065] In some embodiments, obtaining the minimum and maximum pull-out force values ​​of the plaster corresponding to each time segment specifically includes the following technical solutions.

[0066] First, a thermo-coupling method is used to establish a cell material expansion model. By setting the coefficient of thermal expansion and temperature variables, the expansion force changes of the cell in the thickness direction are simulated, and the expansion force data of the large surface area of ​​the cell are output. The volume expansion of the cell during charging and discharging is essentially due to lattice strain caused by lithium-ion insertion and extraction. This strain is macroscopically manifested as the equivalent thermal expansion behavior of the material. By setting the coefficient of thermal expansion associated with the state of charge and applying the corresponding temperature variables, the expansion deformation and stress distribution of the cell at different cycling stages can be efficiently simulated using the thermo-coupling analysis framework. The large surface area of ​​the cell refers to the two principal planes in the thickness direction of the cell. The expansion force data on this plane directly reflects the squeezing effect of the cell on the module and the battery pack, and serves as the input boundary condition for subsequent force transmission analysis. The thermo-coupling method avoids the need to establish a complex electrochemical-mechanical coupling model, significantly reducing the modeling and calculation difficulty while ensuring engineering accuracy.

[0067] Secondly, the cell's large-area expansion force data is aligned with the corresponding maximum and minimum expansion force values ​​for that segment to determine the corresponding time step, and the force experienced by each pad at that time step is output. The cell's large-area expansion force data is a continuous sequence that changes with time steps, while the corresponding maximum and minimum expansion force values ​​are characteristic values ​​extracted from the cell simulation curve in the previous stage. By aligning the two, that is, finding the time step corresponding to the moment in the large-area expansion force data that is equal to the characteristic value, the specific transient states of the cell when it is in the maximum and minimum expansion states can be determined. Module-level simulation is run at this time step, and the pull-out force experienced by each pad in that transient state is output, thereby obtaining the force data of each pad under extreme load conditions. This alignment operation ensures that the boundary conditions applied by the module simulation correspond precisely in time to the actual expansion behavior of the cell, avoiding errors caused by arbitrarily selecting time steps.

[0068] Finally, the force on each pad under the maximum expansion force value is subtracted from the force under the minimum expansion force value to obtain the force difference of each pad. The pad with the largest force difference is selected, and the force on that pad under the maximum expansion force value is taken as the maximum pull-out force value corresponding to that segment, and the force on that pad under the minimum expansion force value is taken as the minimum pull-out force value corresponding to that segment. Within the same segment, different pads experience different pull-out force amplitudes due to differences in position and force transmission path. The force difference of each pad under the maximum expansion state and the minimum expansion state is calculated. This difference represents the amplitude of pull-out force variation experienced by that pad within that segment, i.e., the stress amplitude. Among all pads, the pad with the largest force difference means that it bears the most severe fatigue load and is the most vulnerable to fatigue cracking. By taking the extreme values ​​of the bar piece as the maximum and minimum pull-out force values ​​corresponding to that segment, the load of the most dangerous bar piece is essentially used as the representative load of that segment. This ensures that the load conditions of the subsequent fatigue test cover the weakest link in the structure, thus making the life assessment results more conservative.

[0069] By simulating the cell expansion behavior using a thermo-coupling method, determining the time step corresponding to the characteristic load through data alignment, and selecting the most severely stressed pad as a representative by maximizing the difference, a refined conversion method from cell expansion force to pad pull-out force was established. This method uses the load of the most dangerous pad as the output, ensuring the conservatism and envelope of the fatigue test load, thus improving the reliability of life assessment and engineering safety.

[0070] In some embodiments, the preset lifespan termination condition is when the cell capacity decays to 80% of the rated capacity.

[0071] During long-term charge-discharge cycles, the usable capacity of a battery cell gradually decreases due to factors such as loss of active materials, increase in internal resistance, and decomposition of the electrolyte. When the capacity decays to 80% of the rated capacity, the battery cell is generally considered to have reached the end of its service life. At this point, the cell's expansion behavior, internal resistance characteristics, and thermal characteristics all undergo significant changes.

[0072] In the field of power batteries, an 80% capacity retention rate is widely accepted as the industry standard for battery retirement or replacement. This value takes into account factors such as safety, economy, and user experience. Using the 80% capacity decay point as the preset lifespan termination condition means that the plastic cracking life assessed by this method covers the entire life cycle of the cell from brand new to retired, making the lifespan prediction results match the actual battery usage cycle.

[0073] The total number of cycles obtained by performing expansion force simulation under this termination condition represents the total number of effective cycles experienced by the cell during normal use. Based on this, load spectrum is divided and fatigue tests are conducted to ensure that the life assessment of the upper plastic covers the entire mechanical load history throughout the battery's service life, avoiding the problem of the assessment cycle being too short or too long due to improper selection of termination conditions.

[0074] In some embodiments, in step S5, the aged top cover sample is subjected to fatigue pull-out tests on the corresponding plates according to the minimum and maximum pull-out force values ​​and the number of cycles. Specifically, the following technical solutions are included.

[0075] First, the aged top cover sample is clamped in a fatigue testing machine, and the minimum and maximum pull-out forces corresponding to each segment are applied sequentially according to the life cycle sequence, with the corresponding number of cycles applied to each segment until all segments are loaded. This step couples the two independent clues obtained in the previous step—the top cover sample after temperature and humidity aging and the load spectrum obtained through simulation—at the physical level. The aged top cover sample represents the material state of the plastic on the battery cell at the end of its service life, and its mechanical properties have significantly degraded, while the load spectrum represents the cyclic mechanical loads borne by the battery cell throughout its entire life cycle.

[0076] Superimposing these two elements on a fatigue testing machine essentially replicates, in a laboratory environment, the synergistic effect of environmental aging and cyclic mechanical loading simultaneously experienced by the upper plastic component during actual battery use. Loads are applied sequentially according to the battery's lifespan: first, the first load representing the early cycle is applied, then the second load is automatically applied, and so on until the last load. This sequential loading method simulates the complete load journey experienced by the upper plastic component from battery introduction to end of its lifespan. Each load is applied for a corresponding number of cycles, ensuring that the total number of cycles matches the total number of cycles throughout the cell's lifespan, thus guaranteeing that the cumulative fatigue damage is equivalent to actual conditions.

[0077] Secondly, the mechanical response of the connection area between the pad and the upper plastic is monitored in real time during loading, and the segment and cycle number corresponding to the occurrence of anomalies are recorded. The purpose of real-time monitoring is to capture the critical moment when the upper plastic exhibits anomalies during fatigue loading. The connection area between the pad and the upper plastic is the most significant area of ​​stress concentration and a high-risk area for cracking failure in practical applications; therefore, this area is the focus of monitoring. Recording the segment and cycle number corresponding to the occurrence of anomalies allows for accurate identification of which stage of the upper plastic's life cycle and how many cycles it underwent before the anomaly occurred.

[0078] The method compares the number of cycles corresponding to the occurrence of an anomaly with the total number of cycles throughout the cell's lifespan. If the number of cycles at the time of the anomaly is greater than or equal to the total number of cycles, it indicates that the upper plastic layer will not fail within its designed lifespan, thus meeting the lifespan requirements. Conversely, if the number of cycles at the time of the anomaly is less than the total number of cycles, it indicates that the upper plastic layer has already failed before the battery reaches its end-of-life, failing to meet the lifespan requirements. In this way, the reliability of the upper plastic layer in the cell can be quantitatively assessed during the product design phase, providing direct experimental basis for material selection and structural optimization.

[0079] By applying different load spectra sequentially to aged top cover specimens according to their life cycle sequence and conducting fatigue pull-out tests while monitoring the mechanical response in real time during loading, the physical coupling verification of temperature and humidity aging effects and cyclic mechanical loads was achieved. This method completes the cumulative loading of the entire life cycle load spectrum with a single specimen, significantly shortening the test cycle while ensuring damage equivalence. Furthermore, by recording abnormal moments, it enables accurate quantitative assessment of the plastic's lifespan, solving the problem in existing technologies where aging tests and mechanical tests are disconnected and cannot reflect coupled failure mechanisms.

[0080] In some embodiments, the determination of whether the upper plastic has cracked based on the fatigue pull-out test results in step S5 includes the following specific methods.

[0081] Pull-out force-displacement curves were acquired in real time during fatigue pull-out tests. These curves are graphical representations of the force and displacement relationship, recorded synchronously by the fatigue testing machine during loading. The horizontal axis represents displacement, and the vertical axis represents pull-out force. During normal loading, the connection between the upper plastic sheet and the metal plate is in an elastic or plastic deformation state. The pull-out force increases steadily or fluctuates regularly with increasing displacement, resulting in a smooth and continuous curve. Real-time acquisition of this curve allows for recording the mechanical response of every transient state throughout the fatigue loading process at a high sampling frequency, providing complete raw data for subsequent crack determination.

[0082] When the pull-out force-displacement curve exhibits a sudden change in slope or a sharp drop in force, it is determined that cracking has occurred in the upper plastic layer. A sudden change in slope refers to an abrupt change in the slope of the tangent line at a certain point in the curve, such as a sudden shift from an upward trend to a flat or downward trend, indicating an instantaneous change in the structure's stiffness. A sharp drop in force refers to a significant decrease in pull-out force within a very short displacement increment, which often corresponds to internal fracture or debonding within the structure. In the connection area between the upper plastic layer and the bonding pad, once a crack initiates and rapidly propagates, the structure's load-bearing capacity will decrease instantaneously, manifested as a drop in pull-out force that cannot be maintained at its original level. Therefore, sudden changes in slope and sharp drops in force are direct mechanical characteristics of crack initiation and propagation. Compared to visual observation or microscopic examination, this method has higher sensitivity and real-time performance, capturing cracks the instant they appear and avoiding recording errors caused by observation delays.

[0083] By employing the pull-out force-displacement curve as the cracking criterion, this method elevates crack assessment from qualitative observation to quantitative detection, improving the objectivity and accuracy of the judgment. Simultaneously, this approach can be seamlessly integrated with the data acquisition system of a fatigue testing machine, enabling automatic identification and recording of cracking moments, reducing uncertainties caused by manual intervention, and providing a reliable data foundation for subsequent life assessment.

[0084] In some embodiments, the evaluation of the crack life of the plastic on the battery cell under service conditions in step S5 includes the following specific methods.

[0085] The number of cycles corresponding to the occurrence of cracking is compared with the total number of cycles. The number of cycles corresponding to the occurrence of cracking is the cycle count corresponding to the moment of crack initiation determined by real-time monitoring during the fatigue pull-out test. This number of cycles directly reflects the fatigue life that the upper plastic can withstand after experiencing the coupled effects of temperature and humidity aging and cyclic mechanical loads. The total number of cycles is the total number of cycles over the entire life cycle of the cell, obtained through cell expansion force simulation, under preset life end conditions. It represents all charge and discharge cycles that the battery undergoes from its initial use to the end of its life. Comparing the two essentially benchmarks the actual fatigue life of the upper plastic against the designed lifespan of the battery.

[0086] If the number of cycles at which cracking occurs is greater than or equal to the total number of cycles, the upper plastic is deemed to meet the lifespan requirement. This means that the upper plastic has not cracked after experiencing cyclic loading equivalent to the entire battery lifespan, or that cracking occurs precisely at the end of its service life. This indicates that the fatigue strength of the upper plastic is sufficient to cover the entire service life of the battery, and cracking will not lead to insulation failure or short circuits or other safety issues during normal battery use. This determination provides positive confirmation for the material selection and structural design of the upper plastic, indicating that the current design meets reliability requirements.

[0087] If the number of cycles at which cracking occurs is less than the total number of cycles, the top plastic layer is deemed not to meet the lifespan requirements. This means that the top plastic layer has already cracked and failed before the battery reaches the end of its lifespan. During subsequent battery service, the cracks may further propagate and cause serious consequences such as insulation failure, short circuits, or even thermal runaway. This determination suggests that the material formulation, structural design, or manufacturing process of the top plastic layer needs to be optimized and improved to enhance its resistance to fatigue cracking until the lifespan requirements are met.

[0088] Through this comparative judgment logic, this method directly transforms the raw data obtained from fatigue tests into clear engineering conclusions, providing a quantitative and repeatable judgment standard for the reliability assessment of plastic on the battery cell. This avoids the uncertainty caused by relying on experience judgment or safety factor estimation in traditional methods, making the life assessment results more scientific and reliable.

[0089] It should be noted that, in some optional implementations, in order to fully disclose the technical details of this application, some specific parameters and operations that were not described in detail in the foregoing paragraphs are supplemented below.

[0090] Regarding step S2, the detailed process for obtaining the activation energy and humidity coefficient can be as follows: Prepare six sets of top cover samples and place them in six different environments: 85°C / 95%RH, 80°C / 95%RH, 75°C / 95%RH, 85°C / 85%RH, 80°C / 85%RH, and 75°C / 85%RH, respectively. Each set contains 12 samples, which are then divided into four batches. Each batch is taken out at different preset time points for pull-out testing.

[0091] For example, samples can be taken on days 1, 2, 3, and 4 for the 85°C / 95%RH group; and on days 6, 12, 18, and 24 for the 75°C / 85%RH group. After each sampling, a pull-out force test is performed at a uniform stretching rate of 1 mm / min, and the pull-out force value is recorded. The pull-out force value measured for each group is divided by the initial pull-out force of the unaged sample. F The pull-out force retention rate was obtained. f ij A scatter plot was then created with aging time on the x-axis and pull-out force retention rate on the y-axis, and linear or nonlinear fitting was performed. The critical aging time t at f=0.5 was then obtained using interpolation. Substituting the temperature, humidity, and critical aging time t from the six sets of experiments into the linearized equation of the Peck model, a binary linear regression using the least squares method was performed to fit the activation energy. E a and humidity coefficient n The Boltzmann constant here. k Pick .

[0092] For step S4, although the specific number of segments for dividing the expansion force change curve is illustrated using ten segments as an example, this number is not a limitation. It can be flexibly set to other numbers such as 5, 15, or 20 segments, depending on the total number of cycles of the battery cell, the severity of load changes, and the required test accuracy. More segments result in a load spectrum closer to the actual operating conditions, but increase the test complexity; fewer segments have the opposite effect. As a default configuration, using ten segments already provides a good balance between accuracy and efficiency.

[0093] In the module simulation of step S4, when converting the expansion force into equivalent strip pull-out force, a module finite element model needs to be established, including the top cover assembly, strips, cell casing, and internal core. A thermo-coupling method can be used, assigning an anisotropic thermal expansion coefficient that varies with temperature to the core portion of the cell. By changing the temperature value, the charging expansion and discharging contraction of the cell can be simulated equivalently. Using the minimum and maximum expansion force values ​​obtained from the cell expansion force simulation as inputs to calibrate the thermo-coupling model, the model can output the reaction force at the connection between each strip and the electrode post under the corresponding state. This reaction force is the required strip pull-out force.

[0094] Regarding the fatigue pull-out test in step S5, the testing machine used can be a hydraulic servo fatigue testing machine or other types of dynamic mechanical testing machines. During the test, a special fixture needs to be designed. One end of the fixture fixes the top cover plate of the sample, and the other end clamps the electrode plate, ensuring that the loading direction is perpendicular to the plane of the electrode plate to simulate the axial pull-out effect of the cell expansion force through the electrode post on the electrode plate. Load control adopts a force control mode, and the loading waveform is usually a sine wave or a triangular wave. The frequency can be selected between several hertz and tens of hertz according to the actual situation to ensure test efficiency while avoiding dynamic effects.

[0095] The embodiments described above in this application equivalently compress the continuous damage factor of temperature and humidity aging into a time-dimensional performance degradation, and establish the acceleration factor conversion relationship between the target service environment and accelerated test conditions through an accelerated aging model. Simultaneously, the alternating load factor of cyclic expansion force is equivalently decomposed into a stepped force spectrum in the amplitude dimension. Through expansion force simulation and module simulation, the load transfer and discretization from the internal expansion force of the battery cell to the external pull-out force of the top cover are realized. Finally, these two equivalent results are intersected on a fatigue testing machine. Through a time-series coupling strategy of aging first and then loading, the most severe working condition under the combined action of two failure mechanisms in reality is simulated. This solves the technical problem that existing technologies cannot accurately assess the risk of plastic cracking, and has advantages such as accurate assessment, short test cycle, strong operability, and good versatility.

[0096] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting the lifespan of plastic on a battery cell, characterized in that, The steps include the following: S1. Prepare a top cover sample including the upper plastic and the electrode post with the bar plate fixed on it; S2. Accelerated temperature and humidity aging tests were conducted on multiple groups of top cover samples under different temperature and humidity conditions. Pull-out force tests were conducted on each group of aged samples. The activation energy and humidity coefficient of the accelerated aging model were fitted based on the pull-out force test results. The activation energy and humidity coefficient were substituted into the accelerated aging model and the equivalent accelerated aging time was calculated in combination with the target service environment. S3. The top cover sample is subjected to accelerated temperature and humidity aging in a high temperature and high humidity environment within the equivalent accelerated aging time to obtain the aged top cover sample. S4. Perform expansion force simulation on the battery cell to obtain the total number of cycles and load spectrum of the battery cell under the preset life end condition. Substitute the load spectrum into the module for simulation according to the life cycle sequence, and output the minimum and maximum pull-out force values ​​and the number of cycles corresponding to each segment. S5. After aging, the top cover sample is subjected to fatigue pull-out test according to the minimum and maximum pull-out force values ​​and the number of cycles for each section. The results of the fatigue pull-out test are used to determine whether the plastic on the cell has cracked, so as to evaluate the crack life of the plastic on the cell under service environment.

2. The jellyroll life prediction method of claim 1, wherein, In step S2, accelerated temperature and humidity aging tests are conducted on multiple groups of top cover samples under different temperature and humidity conditions. Pull-out force tests are then performed on each group of aged samples. Based on the pull-out force test results, the activation energy and humidity coefficient of the accelerated aging model are fitted, specifically including: Before conducting accelerated temperature and humidity aging tests, the initial pull-out force of the top plate of the unaged top cover sample was obtained. After the accelerated temperature and humidity aging test, the top cover sample under each temperature and humidity condition was sampled and tested for the pull-out force of the sheet in multiple time batches. The pull-out force retention rate corresponding to each time batch was calculated. The pull-out force retention rate is the ratio of the pull-out force after aging to the initial pull-out force. The relationship between the pull-out force retention rate and aging time is fitted, and the critical aging time when the pull-out force retention rate drops to a preset threshold is obtained by interpolation. The activation energy and humidity coefficient are obtained by linear fitting based on the critical aging time, temperature value, and humidity value corresponding to multiple temperature and humidity conditions.

3. The method for predicting the lifespan of plastic on a battery cell as described in claim 2, characterized in that, Step S2 calculates the equivalent accelerated aging time in conjunction with the target service environment, specifically including: Obtain the average temperature, average humidity, and preset service life of the target service environment; Temperature values ​​below the glass transition temperature of the upper plastic and high humidity values ​​are selected as accelerated aging test conditions. The activation energy, humidity coefficient, target service environment parameters, and accelerated aging test conditions are substituted into the accelerated aging model to calculate the equivalent accelerated aging time.

4. The method for predicting the lifespan of plastic on a battery cell as described in claim 2, characterized in that, The preset threshold is 50% of the initial pull-out force; the accelerated aging model is the Peck model, and the activation energy and humidity coefficient are solved by a binary linear fitting method.

5. The method for predicting the lifespan of plastic on a battery cell as described in claim 1, characterized in that, In step S4, the specific steps for obtaining the total number of cycles and load spectrum of the battery cell under the preset end-of-life conditions, segmenting the load spectrum according to the life cycle sequence, inputting it into the module for simulation, and outputting the minimum and maximum pull-out force values ​​of the battery cell for each segment include: Using the preset percentage of cell capacity decay to rated capacity as the preset lifespan termination condition, a cell charge-discharge cycle expansion force simulation was performed to obtain the total number of cycles and the curves of the minimum and maximum expansion forces per cycle as a function of the number of cycles. The change curve is divided into several time segments of equal length according to the life cycle sequence, and each time segment corresponds to the same number of cycles. Extract the minimum and maximum expansion force extreme values ​​corresponding to each time segment, substitute them into the module structure simulation model, and obtain the minimum and maximum pull-out force values ​​of the blister pack corresponding to each time segment.

6. The method for predicting the lifespan of plastic on a battery cell as described in claim 5, characterized in that, The process of obtaining the minimum and maximum pull-out force values ​​for each time segment specifically includes: A thermo-coupling method was used to establish a cell material expansion model. By setting the thermal expansion coefficient and temperature variable, the expansion force change of the cell in the thickness direction was simulated, and the large-area expansion force data of the cell was output. Align the cell surface expansion force data with the corresponding maximum and minimum expansion force values ​​for that segment, determine the corresponding time step, and output the force on each plate at that time step. Subtract the force on each piece of plaster at the maximum expansion force value from the force at the minimum expansion force value to obtain the force difference of each piece of plaster. Take the piece of plaster with the largest force difference, and take the force on that piece of plaster at the maximum expansion force value as the maximum pull-out force value corresponding to that segment, and take the force on that piece of plaster at the minimum expansion force value as the minimum pull-out force value corresponding to that segment.

7. The method for predicting the lifespan of plastic on a battery cell as described in claim 5, characterized in that, In step S4, the preset lifespan termination condition is that the cell capacity decays to 80% of the rated capacity.

8. The method for predicting the lifespan of plastic on a battery cell as described in claim 1, characterized in that, In step S5, the aged top cover sample is subjected to fatigue pull-out tests on the corresponding plates according to the minimum and maximum pull-out force values ​​and the number of cycles for each section. Specifically, this includes: The aged top cover sample is clamped in a fatigue testing machine, and the minimum and maximum pull-out force values ​​corresponding to each segment are applied sequentially according to the life cycle sequence. The corresponding number of cycles is applied to each segment until the loading of all segments is completed. During the loading process, the mechanical response of the connection area between the pad and the upper plastic is monitored in real time, and the segment and cycle number corresponding to the abnormality are recorded.

9. The method for predicting the lifespan of plastic on a battery cell as described in claim 1, characterized in that, Step S5 involves determining whether the upper plastic has cracked based on the fatigue pull-out test results, including: collecting the pull-out force-displacement curve in real time during the fatigue pull-out test; and determining that the upper plastic has cracked when the pull-out force-displacement curve shows a sudden change in slope or a sudden drop in force.

10. The method for predicting the lifespan of plastic on a battery cell as described in claim 1, characterized in that, Step S5 evaluates the crack life of the plastic on the battery cell under service conditions, including: comparing the number of cycles corresponding to the occurrence of cracks with the total number of cycles. If the number of cycles at the occurrence of cracks is greater than or equal to the total number of cycles, the plastic on the cell is determined to meet the life requirements; otherwise, the plastic on the cell is determined not to meet the life requirements.

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