Method and device for predicting battery packaging life, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a battery packaging life prediction method and device, electronic equipment and a storage medium, and is applied to the technical field of batteries. The method comprises the following steps: responding to a prediction request of the battery packaging life; according to the respiratory fatigue experiment data and the creep fatigue experiment data, establishing a recession model of the to-be-tested battery; inputting the target working condition into a recession model to obtain a recession curve, and inputting the target working condition into an internal air pressure model to obtain an internal air pressure change curve; and determining the packaging life of the to-be-tested battery according to the intersection point of the recession curve and the internal air pressure change curve. According to the scheme, the packaging reliability of the battery is influenced by the respiratory fatigue and the creep fatigue, the decline model is established by combining the influence of the respiratory fatigue and the creep fatigue, and the packaging life is determined according to the target working condition, so that the packaging reliability of the battery can be predicted according to the actual use scene of the battery; therefore, the prediction accuracy of the packaging effect is improved in the early stage of battery development.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, electronic device, and storage medium for predicting battery package life. Background Technology

[0002] With the development of battery technology, batteries can provide energy storage and power supply functions in applications such as electric vehicles and communications. Encapsulation technology isolates the battery's internal components, such as the cells and electrolyte, from the external environment to ensure stable battery operation. The reliability of the encapsulation affects the reliability of the battery itself.

[0003] In related technologies, the package life of a battery is predicted through a single test item and fixed test conditions in order to evaluate the package reliability of the battery.

[0004] However, this method suffers from low accuracy. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for predicting battery package lifespan, in order to improve the accuracy of battery package effect prediction.

[0006] In a first aspect, embodiments of this application provide a method for predicting the lifespan of a battery package, comprising: in response to a prediction request for the lifespan of a battery package, acquiring breathing fatigue test data, creep fatigue test data, and an internal pressure model of a battery under test, wherein the prediction request includes a target operating condition of the battery under test; establishing a degradation model of the battery under test based on the breathing fatigue test data and the creep fatigue test data; inputting the target operating condition into the degradation model to obtain a degradation curve, inputting the target operating condition into the internal pressure model to obtain an internal pressure change curve; and determining the package lifespan of the battery under test based on the intersection of the degradation curve and the internal pressure change curve.

[0007] Secondly, embodiments of this application provide a battery package life prediction device, comprising: an acquisition module, configured to acquire, in response to a battery package life prediction request, breathing fatigue test data, creep fatigue test data, and an internal pressure model of a battery under test, wherein the prediction request includes a target operating condition of the battery under test; an establishment module, configured to establish a degradation model of the battery under test based on the breathing fatigue test data and the creep fatigue test data; a generation module, configured to input the target operating condition into the degradation model to obtain a degradation curve, and input the target operating condition into the internal pressure model to obtain an internal pressure change curve; and a prediction module, configured to determine the package life of the battery under test based on the intersection of the degradation curve and the internal pressure change curve.

[0008] Thirdly, embodiments of this application provide a device for predicting the lifespan of a battery package, comprising: a memory and a processor;

[0009] The memory stores computer-executed instructions;

[0010] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0011] Fourthly, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0012] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0013] This application provides a method, apparatus, electronic device, and storage medium for predicting battery package lifespan. The method includes: responding to a battery package lifespan prediction request, acquiring breathing fatigue test data, creep fatigue test data, and an internal pressure model of the battery under test, wherein the prediction request includes a target operating condition for the battery under test; establishing a degradation model for the battery under test based on the breathing fatigue test data and the creep fatigue test data; inputting the target operating condition into the degradation model to obtain a degradation curve; inputting the target operating condition into the internal pressure model to obtain an internal pressure change curve; and determining the package lifespan of the battery under test based on the intersection of the degradation curve and the internal pressure change curve. In this scheme, the battery package reliability is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the package lifespan based on the target operating condition, the battery package reliability can be predicted according to the actual usage scenario of the battery, thereby improving the prediction accuracy of the package effect in the early stages of battery development. Attached Figure Description

[0014] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0015] Figure 1 A schematic diagram illustrating an application scenario of a battery packaging lifetime prediction method provided in this application embodiment;

[0016] Figure 2 A flowchart illustrating a method for predicting battery package lifetime provided in an embodiment of this application;

[0017] Figure 3 A flowchart illustrating another method for predicting battery package lifetime provided in this application embodiment;

[0018] Figure 4 This is a schematic diagram of the battery casing provided in an embodiment of this application;

[0019] Figure 5 A schematic diagram illustrating the establishment of an explosion-proof valve decay model provided in an embodiment of this application;

[0020] Figure 6 A schematic diagram of respiratory fatigue experimental data provided in the embodiments of this application;

[0021] Figure 7 This is a schematic diagram of creep fatigue test data provided in the embodiments of this application;

[0022] Figure 8 A schematic diagram of the internal air pressure test data of the storage aging path provided in an embodiment of this application;

[0023] Figure 9 A schematic diagram of the internal air pressure test data for the cyclic aging path provided in the embodiments of this application;

[0024] Figure 10 A schematic diagram of the lifetime prediction curve provided in the embodiments of this application;

[0025] Figure 11 A schematic diagram of the structure of a battery package lifetime prediction device provided in an embodiment of this application;

[0026] Figure 12 A schematic diagram of another battery package lifetime prediction device provided in an embodiment of this application;

[0027] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0028] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0031] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0032] It should be noted that the battery package lifetime prediction method, apparatus, electronic device and storage medium of this application can be used in the field of battery technology, or in any field other than batteries. The application field of the battery package lifetime prediction method, apparatus, electronic device and storage medium of this application is not limited.

[0033] Figure 1 This is a schematic diagram illustrating an application scenario of a battery packaging life prediction method provided in this application embodiment. An example is given based on the illustrated scenario: a battery is tested, and the time from the start of the test to packaging failure is recorded; this time is then determined as the packaging life.

[0034] For example, by using encapsulation technology, the internal components of the battery are isolated from the external environment through a casing to ensure that the electrochemical reactions inside the battery are not affected by the external environment, thus enabling the battery to charge and discharge normally.

[0035] In actual use, the internal pressure of the battery changes dynamically. When the internal pressure reaches the upper limit of the casing, the battery casing ruptures; or when the internal pressure reaches the upper limit of the explosion-proof valve, the explosion-proof valve opens. When the battery casing ruptures or the explosion-proof valve opens, the internal components of the battery come into contact with the external environment, and the battery cannot function properly. The time between the moment the battery casing ruptures or the explosion-proof valve opens and the initial moment of the test is the encapsulation life.

[0036] Among them, the explosion-proof valve is used to slowly release the pressure inside the battery, to prevent the casing from cracking due to pressure accumulation and causing a more dangerous explosion, and to ensure that the battery failure mode is a controllable and safe pressure release.

[0037] In related technologies, in the early stages of battery development, the battery under test is tested according to a single test item to determine the package life of the battery under test.

[0038] However, the actual usage scenarios of batteries are quite complex, and the test results obtained through a single test item differ from the actual package life of the battery, resulting in low test accuracy.

[0039] The method for predicting battery package life provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0040] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0041] Figure 2 A flowchart illustrating a method for predicting battery package lifetime provided in this application embodiment is shown. The method includes the following steps:

[0042] S201. In response to the prediction request for battery package life, obtain the breathing fatigue test data, creep fatigue test data and internal air pressure model of the battery under test. The prediction request includes the target operating conditions of the battery under test.

[0043] For example, a prediction request is used to trigger a prediction of the package lifetime of the battery under test in order to obtain the package reliability of the battery under test.

[0044] For example, the target operating conditions are determined based on the usage scenario of the battery under test, thereby accurately simulating the battery's usage scenario.

[0045] To illustrate with a scenario example, if the battery under test is used in an electric vehicle, then the target operating conditions are determined based on the specific scenario of the electric vehicle, such as ambient temperature, battery temperature, pressure, daily charge-discharge cycle count, and daily mileage.

[0046] For example, respiratory fatigue test data and creep fatigue test data are data obtained by testing the electrical device under test under preset conditions.

[0047] For example, an electrochemical reaction inside the battery can cause the internal pressure to rise, and excessive internal pressure can cause the battery casing to crack.

[0048] For example, initially, after a battery is manufactured but before it is used, the pressure that the battery casing can withstand is greater than the pressure inside the battery. The battery casing effectively isolates the internal components of the battery from the external environment, ensuring the battery functions properly.

[0049] However, during battery use, the upper limit of pressure that the battery casing can withstand gradually decreases, while the internal pressure of the battery gradually increases. When the internal pressure of the battery reaches the upper limit of the casing's pressure resistance, the casing will rupture, causing the battery to fail.

[0050] For example, the internal pressure model simulates how the internal pressure of a battery changes with the duration of battery use or the number of battery cycles.

[0051] For example, the upper limit of the pressure that the battery casing can withstand is affected by several factors, including at least breathing fatigue and creep fatigue.

[0052] To illustrate with a scenario example, taking lithium-ion batteries as an example, the insertion and extraction of lithium ions in the electrode causes periodic changes in the electrode thickness direction, i.e., the breathing effect. The breathing effect generates cyclic stress, which in turn leads to cumulative microscopic damage to the battery casing and causes breathing fatigue.

[0053] With the help of scenario examples, the gas produced by the electrochemical reaction inside the battery will continuously exert pressure on the battery casing, leading to creep fatigue.

[0054] Optionally, breathing fatigue experiments can be conducted on the battery under test by simulating the breathing effect under different operating conditions to obtain breathing fatigue experimental data. Creep fatigue experiments can be conducted by simulating the pressure of gas generation inside the battery on the battery casing under different operating conditions to obtain creep fatigue experimental data.

[0055] S202. Based on the respiratory fatigue test data and creep fatigue test data, establish a degradation model for the battery under test.

[0056] For example, both breathing fatigue and creep fatigue can cause fatigue damage to the battery casing, and the battery's package life cannot be accurately predicted using a single test item.

[0057] For example, a degradation model established by combining respiratory fatigue test data and creep fatigue test data can simulate the pattern of total damage to the battery casing caused by respiratory fatigue and creep fatigue, thereby accurately predicting the battery's encapsulation life.

[0058] S203. Input the target operating condition into the decay model to obtain the decay curve, and input the target operating condition into the internal pressure model to obtain the internal pressure change curve.

[0059] For example, by generating degradation curves and internal pressure change curves, the battery can be simulated in the early stages of battery development to predict the future degradation of the upper limit of the pressure that the battery casing can withstand, as well as the future changes in the internal pressure.

[0060] For example, the degradation model includes the degradation patterns of the battery casing under multiple operating conditions, with different degradation patterns under different conditions. By inputting the target operating condition into the degradation model, the model is constrained to output only the degradation curve under the target condition, thus accurately simulating the degradation pattern of the battery under test in real-world usage scenarios. The same principle applies to the internal air pressure change curve. This improves the accuracy of the prediction results.

[0061] S204. Determine the package life of the battery under test based on the intersection of the degradation curve and the internal air pressure change curve.

[0062] For example, at the initial moment and for an initial period of time during simulated battery use, the degradation curve is above the internal pressure change curve, indicating that the upper limit of pressure that the battery casing can withstand is greater than the internal pressure of the battery.

[0063] The moment when the degradation curve and the internal pressure change curve intersect (i.e., the point where they meet) indicates that the internal pressure of the battery has reached the upper limit of the pressure that the battery casing can withstand. At this point, the encapsulation fails, and the isolation between the internal battery components and the external environment can no longer be maintained. The duration between the moment corresponding to the intersection and the initial moment is the battery encapsulation lifespan.

[0064] The battery packaging life prediction method provided in this application, in response to a battery packaging life prediction request, acquires breathing fatigue test data, creep fatigue test data, and an internal pressure model of the battery under test. The prediction request includes the target operating conditions of the battery under test. Based on the breathing fatigue test data and creep fatigue test data, a degradation model of the battery under test is established. The target operating conditions are input into the degradation model to obtain a degradation curve, and the target operating conditions are input into the internal pressure model to obtain an internal pressure change curve. The packaging life of the battery under test is determined based on the intersection of the degradation curve and the internal pressure change curve. In this scheme, the battery packaging reliability is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the packaging life based on the target operating conditions, the packaging reliability of the battery can be predicted according to the actual usage scenarios of the battery, thereby improving the prediction accuracy of packaging effect in the early stages of battery development.

[0065] Based on any of the above embodiments, the following, in conjunction with Figure 3 The detailed process of predicting battery package life is explained.

[0066] Figure 3 This is a flowchart illustrating another method for predicting battery package lifetime provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0067] S301. In response to a prediction request for battery package life, acquire the breathing fatigue test data, creep fatigue test data, and internal air pressure model of the battery under test. The prediction request includes the target operating conditions of the battery under test.

[0068] One feasible implementation method is that the breathing fatigue test data includes the breathing fatigue test data of the explosion-proof valve and the breathing fatigue test data of the shell weld; the creep fatigue test data includes the creep fatigue test data of the explosion-proof valve and the creep fatigue test data of the shell weld.

[0069] Below, in conjunction with Figure 4 The battery casing is described.

[0070] Figure 4 This is a schematic diagram of the battery casing provided in an embodiment of this application. Figure 4 As shown, the weakest points of the battery casing under pressure include the explosion-proof valve and the welds. The explosion-proof valve is an active safety device used to prevent violent explosions by releasing pressure. The welds are the passive pressure-bearing structure of the battery casing. The upper limit of the pressure that the welds can withstand is related to the packaging process, and generally the upper limit of the pressure that the welds can withstand is lower than the upper limit of the pressure that other parts of the battery casing can withstand.

[0071] For example, both breathing fatigue and creep fatigue can affect the upper pressure limit of explosion-proof valves. Both breathing fatigue and creep fatigue can also affect the upper pressure limit of welds. Multiple experimental data are obtained to accurately predict the failure patterns of battery casings.

[0072] Specifically, the breathing fatigue test data for explosion-proof valves recorded the degradation law of their opening pressure performance with the number of cycles under simulated battery breathing effects. The creep fatigue test data for explosion-proof valves recorded the degradation law of the valves under pressure caused by continuous gas production inside a simulated battery. The experimental data related to the shell welds were similar.

[0073] Optionally, the breathing fatigue test data and creep fatigue test data can be filtered to remove data that does not significantly affect the battery casing, thereby reducing the computational load for packaging prediction. For example, if creep fatigue data of the casing welds shows that creep fatigue damage to the casing welds is negligible during the battery's lifespan, then the creep fatigue data of the casing welds can be deleted.

[0074] In this feasible implementation, fatigue data of the explosion-proof valve and the weld seam of the battery casing are obtained to comprehensively identify the factors that cause damage to the battery casing, thereby improving the accuracy of the prediction of the encapsulation effect.

[0075] S302. Based on the breathing fatigue test data and creep fatigue test data of the explosion-proof valve, establish a decay model for the explosion-proof valve.

[0076] For example, the explosion-proof valve degradation model is used to represent the degradation pattern of the upper pressure limit that the explosion-proof valve can withstand under different operating conditions. The degradation of the upper pressure limit is caused by breathing fatigue and creep fatigue.

[0077] A feasible implementation method can be used to establish the degradation model of the explosion-proof valve, including: determining the upper limit sample and lower limit sample of the explosion-proof valve based on the breathing fatigue test data and creep fatigue test data of the explosion-proof valve; determining the upper limit breathing fatigue test data corresponding to the upper limit sample and the lower limit breathing fatigue test data corresponding to the lower limit sample from the breathing fatigue test data of the explosion-proof valve; determining the upper limit creep fatigue test data corresponding to the upper limit sample and the lower limit creep fatigue test data corresponding to the lower limit sample from the creep fatigue test data of the explosion-proof valve; determining the upper limit valve opening pressure degradation model based on the upper limit breathing fatigue test data and the upper limit creep fatigue test data; and determining the lower limit valve opening pressure degradation model based on the lower limit breathing fatigue test data and the lower limit creep fatigue test data; and finally, determining the upper limit valve opening pressure degradation model and the lower limit valve opening pressure degradation model as the explosion-proof valve degradation model.

[0078] For example, the upper limit sample of the explosion-proof valve is the explosion-proof valve sample with the least significant degradation after excluding obviously abnormal samples from a batch of explosion-proof valve samples corresponding to the experimental data. Similarly, the lower limit sample of the explosion-proof valve is the explosion-proof valve sample with the most significant degradation.

[0079] For example, based on the upper limit sample of the explosion-proof valve, scenarios where the explosion-proof valve fails before the housing weld can be predicted. In battery design, the upper limit of the explosion-proof valve's pressure tolerance is lower than the upper limit of the housing weld's pressure tolerance. When the internal pressure of the battery increases, the upper limit of the explosion-proof valve's pressure tolerance is reached first, allowing the explosion-proof valve to achieve its safe pressure relief function. However, if the damage levels of the explosion-proof valve and the housing weld are inconsistent, a situation may arise where the upper limit of the explosion-proof valve's pressure tolerance exceeds the upper limit of the housing weld's pressure tolerance, in which case the safe pressure relief function fails.

[0080] By comparing the maximum pressure withstand capability of the explosion-proof valve sample with that of the housing weld, it can be verified whether the explosion-proof valve will still fail before the housing weld in the most unfavorable scenario (i.e., when the explosion-proof valve is least likely to open). If the explosion-proof valve sample can ensure failure before the housing weld, then the safety of the entire batch of explosion-proof valves has been verified to the highest level.

[0081] For example, the scenario in which the explosion-proof valve will open can be predicted based on the lower limit sample of the explosion-proof valve. When the internal pressure of the battery increases, if it reaches the upper limit of the pressure that the explosion-proof valve can withstand, the explosion-proof valve will open to achieve the goal of safe pressure relief.

[0082] With a scenario example, using the lower limit sample of the explosion-proof valve to predict the opening of the explosion-proof valve yields the shortest package lifespan for that batch of batteries. Using the shortest package lifespan as the package lifespan of the batch of batteries ensures the safety and conservatism of the prediction results.

[0083] Below, in conjunction with Figure 5 The establishment of the explosion-proof valve decay model is explained.

[0084] Figure 5 This is a schematic diagram illustrating the establishment of a degradation model for an explosion-proof valve, provided as an embodiment of this application. Figure 5 As shown, the upper and lower limit samples of the explosion-proof valve were determined from fatigue test data. Fatigue test data corresponding to the upper and lower limit samples were screened separately. Based on their respective fatigue test data, upper limit opening pressure decay models and lower limit opening pressure decay models were established. The upper limit opening pressure decay model represents the decay law of the upper limit pressure of the explosion-proof valve sample, and the lower limit opening pressure decay model represents the decay law of the upper limit pressure of the explosion-proof valve sample. The explosion-proof valve decay model includes both the upper limit opening pressure decay model and the lower limit opening pressure decay model.

[0085] In this feasible implementation, by distinguishing between the upper limit sample and the lower limit sample of the explosion-proof valve, the performance boundary sample can be predicted, simulating the worst-case scenario in mass production. This allows the final explosion-proof valve degradation model to cover product fluctuations, thereby improving the accuracy of the packaging effect prediction.

[0086] A feasible implementation method for determining the upper limit valve opening pressure decay model includes: identifying multiple test conditions; extracting data from upper limit breathing fatigue test data based on the multiple test conditions to obtain multiple first experimental data; fitting the multiple first experimental data to obtain a first decay model, which represents the mapping relationship between valve opening pressure decay value and cycle number under different test conditions; extracting data from upper limit creep fatigue test data based on the multiple test conditions to obtain multiple second experimental data; fitting the multiple second experimental data to obtain a second decay model, which represents the mapping relationship between valve opening pressure decay value and duration under different test conditions; and determining the upper limit valve opening pressure decay model based on the first and second decay models.

[0087] For example, multiple first experimental data include the number of cycles the battery completes until failure under different test conditions by applying multiple breathing pressures. The breathing pressure is the pressure that simulates the breathing fatigue effect.

[0088] Below, in conjunction with Figure 6 Explain the experimental data on respiratory fatigue.

[0089] Figure 6 This is a schematic diagram of respiratory fatigue experimental data provided in an embodiment of this application. Figure 6 As shown, the test conditions included multiple temperatures. Based on the data from the first experiment, it can be seen that at the same temperature, the higher the breathing pressure, the faster the battery fails and the fewer the battery cycle counts. Under the same breathing pressure, the higher the temperature, the faster the battery fails and the fewer the battery cycle counts.

[0090] For example, multiple first experimental data are fitted to quantify the damage to the explosion-proof valve caused by breathing fatigue per charge-discharge cycle under different test parameters, thereby establishing a first decay model.

[0091] For example, multiple first experimental data include the predicted time until battery failure obtained by applying multiple holding pressures to the battery under different test conditions. The holding pressure is the pressure used to simulate creep fatigue effects.

[0092] Below, in conjunction with Figure 7 The experimental data on creep fatigue are explained.

[0093] Figure 7This is a schematic diagram of creep fatigue test data provided in an embodiment of this application. Figure 7 As shown, the test conditions included multiple temperatures. According to the second experimental data, at the same temperature, a higher holding pressure results in a faster creep rate, faster battery failure, and a smaller number of battery cycle times. Under the same holding pressure, higher temperatures lead to faster battery failure and a smaller number of battery cycle times.

[0094] For example, multiple second experimental data are fitted to quantify the amount of damage to the explosion-proof valve caused by creep fatigue per unit time under different test parameters, thereby establishing a second decay model.

[0095] For example, the first decay model and the second decay model are used together as the valve opening pressure decay model of the explosion-proof valve to predict the overall decay law of the upper limit sample valve opening pressure of the explosion-proof valve.

[0096] It should be noted that the determination process for the lower limit opening pressure decay model of the explosion-proof valve lower limit sample is similar.

[0097] In this feasible implementation, damage caused by two different physical mechanisms, breathing fatigue and creep fatigue, is modeled independently to accurately reflect the complex laws governing the performance degradation of explosion-proof valves, thereby improving the accuracy of predicting the sealing effect.

[0098] One feasible implementation method is to determine the upper limit valve opening pressure decay model by: determining the ratio of daily driving mileage to fully charged driving mileage as the daily equivalent cycle number; converting the first decay model according to the daily equivalent cycle number to obtain a converted decay model, which represents the mapping relationship between the valve opening pressure decay value and duration, temperature, and breathing pressure; and coupling the converted decay model with the second decay model to obtain the upper limit valve opening pressure decay model.

[0099] The target operating conditions include daily driving mileage and driving mileage on a full charge.

[0100] For example, the first decay model uses the cycle number parameter, and the second decay model uses the time parameter. The parameters and dimensions are unified through model transformation, thereby coupling the models.

[0101] To illustrate with a scenario example, taking a lithium-ion battery as an example, the "breathing effect" refers to the periodic changes in the thickness direction of a lithium-ion battery during charge-discharge cycles due to lithium-ion insertion / extraction. This periodic expansion and contraction applies cyclic stress to the gaps in the battery casing. Fatigue damage to the battery casing under cyclic stress is cumulative. Each completed charge-discharge cycle subjects the battery casing to one stress cycle, resulting in minor damage. The cumulative amount of damage is directly proportional to the number of cycles.

[0102] To illustrate with a scenario example, creep is the effect of pressure caused by gas buildup inside the battery acting on the battery casing. Under this pressure, the battery casing undergoes slow, continuous creep. This creep intensifies with increasing exposure time. The cumulative damage is directly proportional to the duration of exposure.

[0103] For example, by calculating the ratio of daily driving mileage to fully charged driving mileage based on the target operating conditions of the battery under test, the discontinuous charging and discharging behavior distributed over time in actual use can be quantified into a standardized, daily average equivalent number of cycles.

[0104] For example, conversion is performed using the equivalent number of daily cycles to unify the cycle count and time parameter.

[0105] Optionally, coupling can be achieved through linear superposition.

[0106] In this feasible implementation, the converted time-related respiratory fatigue damage and time-related creep fatigue damage are linearly superimposed on the same time coordinate, thereby achieving quantitative coupling of complex failure physics and improving the prediction accuracy of encapsulation effect.

[0107] S303. Fit the breathing fatigue test data of the shell weld to obtain the first weld strength decay model of the shell weld.

[0108] For example, the experimental data of breathing fatigue of the shell weld are fitted to quantify the damage of breathing fatigue to the shell weld per charge-discharge cycle under different test parameters, thereby establishing a first weld strength decay model.

[0109] S304. Fit the creep fatigue test data of the shell weld to obtain the second weld strength decay model of the shell weld.

[0110] For example, the creep fatigue test data of the shell weld is fitted to quantify the damage to the shell weld caused by creep fatigue per unit time under different test parameters, thereby establishing a second weld strength decay model.

[0111] S305. Determine the shell weld decay model based on the first weld strength decay model and the second weld strength decay model.

[0112] For example, the first weld strength decay model uses the cycle number parameter and the time parameter. The equivalent cycle number per day is calculated to transform the first weld strength decay model to unify the parameters and dimensions, thereby coupling the models.

[0113] Optionally, a shell weld decay model can be obtained by coupling through linear superposition.

[0114] Based on the scenario example, the strength decay model of the first weld can be expressed as: ={temperature, number of cycles, breathing pressure}, the second weld strength decay model can be expressed as: ={temperature, duration, holding pressure}, the shell weld decay model can be expressed as P= + .

[0115] S306. The degradation model of the explosion-proof valve and the degradation model of the shell weld are determined as the degradation model of the battery under test.

[0116] For example, the degradation model of the explosion-proof valve and the degradation model of the shell weld are jointly determined as the degradation model of the battery under test.

[0117] Based on the above implementation methods, multiple degradation modes of the battery casing are analyzed in a unified manner to cover multiple failure modes of the battery, thereby improving the accuracy of the prediction of the packaging effect.

[0118] S307. Input the target operating condition into the decay model to obtain the decay curve, and input the target operating condition into the internal pressure model to obtain the internal pressure change curve.

[0119] One feasible implementation method is to obtain the decay curve by: inputting the target operating condition into the upper limit valve opening pressure decay model to obtain the upper limit explosion-proof valve decay curve; inputting the target operating condition into the lower limit valve opening pressure decay model to obtain the lower limit explosion-proof valve decay curve; inputting the target operating condition into the shell weld decay model to obtain the shell weld decay curve; and determining the upper limit explosion-proof valve decay curve, the lower limit explosion-proof valve decay curve, and the shell weld decay curve as the decay curve.

[0120] For example, the upper limit opening pressure decay model includes the decay curves of the explosion-proof valve under different test parameters. By substituting the target operating condition, the upper limit explosion-proof valve decay curve under the target operating condition is output, which better reflects the actual usage scenario of the battery under test.

[0121] It should be noted that the generation process of the lower limit explosion-proof valve decay curve and the shell weld decay curve is the same.

[0122] For example, the degradation curves of the upper limit explosion-proof valve, the lower limit explosion-proof valve, and the shell weld are combined as a single degradation curve to cover multiple degradation patterns of the battery shell.

[0123] In this feasible implementation, by generating multiple degradation curves, the prediction is no longer a single prediction for a certain idealized component, but can cover multiple extreme cases of the battery, thereby improving the prediction accuracy of the packaging effect.

[0124] One feasible implementation method involves the following steps in establishing the internal pressure model: acquiring internal pressure experimental data for the storage aging path and the cycle aging path of the battery under test; fitting the internal pressure experimental data for the storage aging path to obtain a first internal pressure model; fitting the internal pressure experimental data for the cycle aging path to obtain a second internal pressure model; and establishing the internal pressure model based on the first and second internal pressure models.

[0125] For example, the battery has a sealed structure. During battery use, gas will be generated inside the battery, causing the internal gas pressure to rise. The internal gas pressure experimental data is the data on the changes in internal gas pressure of the battery under different test conditions.

[0126] For example, the internal gas pressure changes of the battery under storage and charge-discharge cycle conditions for the same duration are different. The internal gas pressure experimental data under the storage aging path and the cycle aging path are tested respectively to analyze the independent influence of different aging mechanisms on gas generation behavior.

[0127] Below, in conjunction with Figure 8 The internal air pressure test data of the storage aging path are explained.

[0128] Figure 8 This is a schematic diagram illustrating the internal air pressure test data of the storage aging path provided in an embodiment of this application. Figure 8 As shown, the test conditions included multiple temperatures. Based on the internal pressure experimental data from the storage aging path, it can be seen that at the same temperature, the longer the storage time, the higher the internal pressure of the battery. For the same storage time, the higher the temperature, the higher the internal pressure of the battery.

[0129] For example, a first internal pressure model is obtained by fitting internal pressure experimental data from multiple storage aging paths to quantify the increase in internal pressure of the battery per unit of storage time under different test parameters.

[0130] With the help of scenario examples, the increase in internal gas pressure of the battery during the storage aging process may be caused by electrolyte decomposition, etc.

[0131] Below, in conjunction with Figure 9 The internal air pressure experimental data of the cyclic aging path are explained.

[0132] Figure 9 This is a schematic diagram of the internal air pressure test data for the cyclic aging path provided in an embodiment of this application. Figure 9 As shown, the test conditions included multiple temperatures and a fixed charge / discharge rate (1C). The experimental data shows that at the same temperature, the more charge / discharge cycles the battery completes, the higher the internal gas pressure. At the same number of cycles, the higher the temperature, the higher the internal gas pressure of the battery.

[0133] For example, a second internal pressure model is obtained by fitting the internal pressure experimental data of multiple cyclic aging paths to quantify the increase in internal pressure of the battery per charge-discharge cycle under different test parameters.

[0134] Based on scenario examples, the increase in internal battery pressure during storage aging may be caused by chemical side reactions and breathing effects.

[0135] For example, the first internal pressure model and the second internal pressure model are transformed to unify parameters and dimensions, and then the models are coupled to obtain the internal pressure model.

[0136] In this feasible implementation, by acquiring experimental data from the storage aging path and the cycle aging path respectively, and then establishing and coupling the first internal gas pressure model and the second internal gas pressure model respectively, the gas generation law of the battery under different states is accurately reflected, thereby improving the prediction accuracy of the encapsulation effect.

[0137] As an example, the internal pressure change curve can be determined by inputting the target operating condition into the internal pressure model to obtain the internal pressure change curve.

[0138] The following scenario example illustrates this: target operating conditions include average daily driving time, average daily storage time, battery temperature, and ambient temperature. These target operating conditions are input into an internal pressure model, which predicts the change in internal pressure over time corresponding to the target operating conditions, i.e., the internal pressure change curve.

[0139] Based on the above implementation methods, an internal pressure change curve is generated according to the target operating conditions so that the internal pressure change curve conforms to the actual usage scenario of the battery under test, thereby improving the accuracy of the prediction.

[0140] One feasible implementation method is to determine the encapsulation life of the battery under test by the following steps: determining the intersection of the internal gas pressure change curve and the lower limit explosion-proof valve decay curve as the first intersection point, and determining the first abscissa value corresponding to the first intersection point; determining the intersection of the shell weld decay curve and the upper limit explosion-proof valve decay curve as the second intersection point, and determining the second abscissa value corresponding to the second intersection point; and determining the minimum value between the first abscissa value and the second abscissa value as the encapsulation life.

[0141] For example, the first intersection point indicates that the pressure generated inside the battery has reached the real-time opening pressure of the lower limit sample of the explosion-proof valve, which is the most sensitive and fastest deteriorating. At this point, the explosion-proof valve will open, and the battery will reach the end of its life.

[0142] For example, the second intersection point indicates that even the most sluggish and stable explosion-proof valve sample in the batch has reached an opening pressure equal to the strength of the degraded housing weld. At this point, a failure mode may occur where the dangerous housing weld ruptures before the explosion-proof valve, and the battery reaches its lifespan limit.

[0143] Below, in conjunction with Figure 10 The life prediction curve is explained.

[0144] Figure 10 This is a schematic diagram of the lifetime prediction curve provided in an embodiment of this application. Figure 10 As shown, the horizontal axis represents time, and the vertical axis represents pressure. The internal gas pressure of the battery shows an upward trend. The internal gas pressure change curve indicates that with the accumulation of time or charge-discharge cycles, the gas generated inside the battery due to electrochemical reactions gradually increases, leading to a slow increase in pressure. The lower limit explosion-proof valve decay curve is the decay curve of the explosion-proof valve with a lower opening pressure.

[0145] Initially, the lower limit explosion-proof valve decay curve lies below the internal pressure change curve, indicating that the explosion-proof valve can withstand the internal pressure of the battery, and the battery is operating normally. As the battery is used, the lower limit explosion-proof valve decay curve decreases, and the internal pressure increases. When the internal pressure change curve intersects with the lower limit explosion-proof valve decay curve, it indicates that the internal pressure of the battery has reached the upper limit that the explosion-proof valve can withstand, and the battery has reached the end of its lifespan. The time corresponding to the intersection point is the package lifespan.

[0146] Initially, the casing weld degradation curve is designed to be located above the upper limit explosion-proof valve degradation curve, ensuring that the internal pressure of the battery allows the explosion-proof valve to reach its maximum capacity before depressurization, thus enabling the valve to safely release pressure. As the battery is used, the strength of the casing weld gradually decreases due to fatigue. When the upper limit explosion-proof valve degradation curve intersects with the casing weld degradation curve, it indicates that the explosion-proof valve can no longer release pressure before the casing weld, signifying the battery has reached the end of its lifespan.

[0147] For example, the first horizontal axis value represents the predicted time corresponding to the first intersection point, and the second horizontal axis value represents the predicted time corresponding to the second intersection point. Based on safety redundancy and conservative design, the minimum value between the first and second horizontal axis values ​​is determined as the package lifetime.

[0148] In this feasible implementation, by determining the first intersection point and the second intersection point respectively, a comprehensive analysis of multiple failure modes of the battery can be performed to determine the failure mode that occurs first, thereby improving the accuracy of the prediction of the packaging effect.

[0149] S308. Determine the package life of the battery under test based on the intersection of the degradation curve and the internal air pressure change curve.

[0150] One feasible implementation method, after determining the package life of the battery under test, may further include: collecting the real-time operating parameters of the battery under test; and correcting the degradation model and internal pressure model based on the real-time operating parameters.

[0151] For example, after the battery under test is put into use, the real-time operating parameters of the battery under test are continuously or periodically collected through the Battery Management System (BMS) or other sensors.

[0152] For example, real-time operating parameters include, but are not limited to, ambient temperature, battery temperature, pressure, daily charge / discharge cycles, and daily mileage. These real-time operating parameters reflect the battery's actual usage condition, which may deviate from its initial target operating conditions.

[0153] For example, the collected real-time operating parameters are used as new inputs and fed back into the established decay model and internal pressure model for correction.

[0154] Optionally, the correction process may include, but is not limited to:

[0155] Parameter calibration involves fine-tuning individual coefficients or parameters in the model using real-time operating parameters, so that the model's predicted output better matches the actual performance degradation pattern of the battery.

[0156] Model updates involve refitting the model using real-time working parameters or iteratively updating the model using adaptive algorithms.

[0157] In this feasible implementation, by introducing real-time operating parameters for correction, the error between model prediction and actual decline can be continuously reduced, making the life prediction results more and more accurate over time.

[0158] Figure 11 This is a schematic diagram of a battery package lifetime prediction device provided in an embodiment of this application. Figure 11 As shown, the battery package lifetime prediction device 110 may include: an acquisition module 111, an establishment module 112, a generation module 113, and a prediction module 114.

[0159] The acquisition module 111 is used to acquire the breathing fatigue test data, creep fatigue test data and internal air pressure model of the battery under test in response to the prediction request of the battery package life. The prediction request includes the target operating conditions of the battery under test.

[0160] Module 112 is established to build a degradation model for the battery under test based on respiratory fatigue test data and creep fatigue test data.

[0161] The generation module 113 is used to input the target operating condition into the decay model to obtain the decay curve, and to input the target operating condition into the internal pressure model to obtain the internal pressure change curve.

[0162] The prediction module 114 is used to determine the package life of the battery under test based on the intersection of the degradation curve and the internal air pressure change curve.

[0163] Optionally, module 111 can be executed. Figure 2 S201 in the embodiment.

[0164] Optionally, module 112 can be executed. Figure 2 S202 in the embodiment.

[0165] Optionally, the generation module 113 can execute Figure 2 S203 in the embodiment.

[0166] Optionally, the prediction module 114 can perform... Figure 2 S204 in the embodiment.

[0167] Based on the above implementation methods, the packaging reliability of the battery is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the packaging life according to the target operating conditions, the packaging reliability of the battery can be predicted according to the actual use scenario of the battery, thereby improving the prediction accuracy of the packaging effect in the early stage of battery development.

[0168] It should be noted that the battery package lifetime prediction device shown in the embodiments of this application can execute the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0169] In one possible implementation, the breathing fatigue test data includes breathing fatigue test data of the explosion-proof valve and breathing fatigue test data of the shell weld; the creep fatigue test data includes creep fatigue test data of the explosion-proof valve and creep fatigue test data of the shell weld.

[0170] In one possible implementation, module 112 is specifically used for:

[0171] Based on the breathing fatigue test data and creep fatigue test data of explosion-proof valves, an explosion-proof valve decay model is established.

[0172] By fitting the breathing fatigue test data of the shell weld, the first weld strength decay model of the shell weld is obtained;

[0173] By fitting the experimental data of creep fatigue of the shell weld, a second weld strength decay model of the shell weld is obtained.

[0174] The shell weld decay model is determined based on the first weld strength decay model and the second weld strength decay model.

[0175] The degradation models of the explosion-proof valve and the shell weld were determined as the degradation models of the battery under test.

[0176] In one possible implementation, module 112 is specifically used for:

[0177] Based on the breathing fatigue test data and creep fatigue test data of explosion-proof valves, the upper limit sample and lower limit sample of explosion-proof valves were determined.

[0178] From the breathing fatigue test data of explosion-proof valves, determine the upper limit breathing fatigue test data corresponding to the upper limit sample of explosion-proof valves, and the lower limit breathing fatigue test data corresponding to the lower limit sample of explosion-proof valves;

[0179] From the creep fatigue test data of explosion-proof valves, determine the upper limit creep fatigue test data corresponding to the upper limit sample of the explosion-proof valve, and the lower limit creep fatigue test data corresponding to the lower limit sample of the explosion-proof valve;

[0180] Based on the upper limit respiratory fatigue test data and the upper limit creep fatigue test data, the upper limit valve opening pressure decay model was determined.

[0181] Based on the lower limit respiratory fatigue test data and the lower limit creep fatigue test data, the lower limit valve opening pressure decay model was determined.

[0182] The upper limit valve opening pressure decay model and the lower limit valve opening pressure decay model are determined as the decay model for explosion-proof valves.

[0183] In one possible implementation, module 112 is specifically used for:

[0184] Multiple test conditions were determined;

[0185] Based on multiple test conditions, data were extracted from the upper limit respiratory fatigue test data to obtain multiple first test data;

[0186] The first decay model is obtained by fitting multiple first experimental data. The first decay model is used to represent the mapping relationship between the valve opening pressure decay value and the number of cycles under different test conditions.

[0187] Based on multiple test conditions, data were extracted from the upper limit creep fatigue test data to obtain multiple second test data;

[0188] By fitting multiple second experimental data, a second decay model is obtained. The second decay model is used to represent the mapping relationship between valve opening pressure decay value and time under different test conditions.

[0189] Based on the first and second recession models, the upper limit valve opening pressure recession model is determined.

[0190] In one possible implementation, module 112 is specifically used for:

[0191] The ratio of daily driving mileage to fully charged driving mileage is determined as the equivalent number of daily cycles.

[0192] Based on the daily equivalent cycle count, the first decay model is transformed to obtain the transformed decay model, which represents the mapping relationship between the valve opening pressure decay value and duration, temperature, and breathing pressure.

[0193] By coupling the conversion decay model with the second decay model, the upper limit valve opening pressure decay model is obtained.

[0194] Figure 12 This is a schematic diagram of another battery package lifetime prediction device provided in an embodiment of this application. Figure 11 Based on the illustrated embodiments, as Figure 12 As shown, the prediction 110 for the battery package lifespan also includes: an execution module 115, a simulation module 116, an analysis module 117, and a correction module 118.

[0195] Execution module 115 is used for:

[0196] Acquire internal pressure test data for the storage aging path and the cycle aging path of the battery under test;

[0197] The internal pressure experimental data of the storage aging path were fitted to obtain the first internal pressure model;

[0198] The internal pressure experimental data of the cyclic aging path were fitted to obtain the second internal pressure model.

[0199] An internal pressure model is established based on the first and second internal pressure models.

[0200] Simulation module 116 is used for:

[0201] Input the target operating condition into the upper limit valve opening pressure decay model to obtain the upper limit explosion-proof valve decay curve;

[0202] Input the target operating condition into the lower limit valve opening pressure decay model to obtain the lower limit explosion-proof valve decay curve;

[0203] Input the target working condition into the shell weld decay model to obtain the shell weld decay curve;

[0204] The decay curves of the upper limit explosion-proof valve, the lower limit explosion-proof valve, and the shell weld are defined as decay curves.

[0205] Analysis module 117 is used for:

[0206] The intersection of the internal air pressure change curve and the decay curve of the lower limit explosion-proof valve is determined as the first intersection point, and the first abscissa value corresponding to the first intersection point is determined.

[0207] The intersection of the decay curve of the shell weld and the decay curve of the upper limit explosion-proof valve is determined as the second intersection point, and the second abscissa value corresponding to the second intersection point is determined.

[0208] The minimum value between the first and second horizontal coordinates is determined as the package lifetime.

[0209] Correction module 118, used for:

[0210] Collect real-time operating parameters of the battery under test;

[0211] The decay model and internal pressure model are corrected based on real-time operating parameters.

[0212] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 13 As shown, the electronic device includes:

[0213] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0214] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0215] The memory 292, as a non-volatile computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0216] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 292 may include high-speed random access memory and may also include non-volatile memory.

[0217] Based on the above implementation methods, the packaging reliability of the battery is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the packaging life according to the target operating conditions, the packaging reliability of the battery can be predicted according to the actual use scenario of the battery, thereby improving the prediction accuracy of the packaging effect in the early stage of battery development.

[0218] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0219] Based on the above implementation methods, the packaging reliability of the battery is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the packaging life according to the target operating conditions, the packaging reliability of the battery can be predicted according to the actual use scenario of the battery, thereby improving the prediction accuracy of the packaging effect in the early stage of battery development.

[0220] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0221] Based on the above implementation methods, the packaging reliability of the battery is affected by breathing fatigue and creep fatigue. By combining the effects of breathing fatigue and creep fatigue to establish a degradation model and determining the packaging life according to the target operating conditions, the packaging reliability of the battery can be predicted according to the actual use scenario of the battery, thereby improving the prediction accuracy of the packaging effect in the early stage of battery development.

[0222] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0223] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0224] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0225] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0226] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0227] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0228] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0229] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0230] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for predicting battery package life, characterized in that, include: In response to a prediction request for battery package life, the system acquires breathing fatigue test data, creep fatigue test data, and internal pressure model of the battery under test, wherein the prediction request includes the target operating conditions of the battery under test. Based on the respiratory fatigue test data and the creep fatigue test data, a degradation model for the battery under test is established. Input the target operating condition into the decay model to obtain the decay curve, and input the target operating condition into the internal pressure model to obtain the internal pressure change curve. The package life of the battery under test is determined based on the intersection of the degradation curve and the internal air pressure change curve.

2. The method according to claim 1, characterized in that, The breathing fatigue test data includes breathing fatigue test data of explosion-proof valves and breathing fatigue test data of shell welds; The creep fatigue test data includes creep fatigue test data of explosion-proof valves and creep fatigue test data of shell welds.

3. The method according to claim 2, characterized in that, Based on the respiratory fatigue test data and the creep fatigue test data, a degradation model for the battery under test is established, including: Based on the breathing fatigue test data and creep fatigue test data of the explosion-proof valve, a decay model of the explosion-proof valve is established. By fitting the breathing fatigue test data of the shell weld, a first weld strength decay model of the shell weld is obtained; By fitting the creep fatigue test data of the shell weld, a second weld strength decay model of the shell weld is obtained; Based on the first weld strength decay model and the second weld strength decay model, determine the shell weld decay model; The degradation model of the explosion-proof valve and the degradation model of the shell weld are determined as the degradation model of the battery under test.

4. The method according to claim 3, characterized in that, Based on the breathing fatigue test data and creep fatigue test data of the explosion-proof valve, a degradation model for the explosion-proof valve is established, including: Based on the breathing fatigue test data and creep fatigue test data of the explosion-proof valve, the upper limit sample and the lower limit sample of the explosion-proof valve are determined. From the breathing fatigue test data of the explosion-proof valve, determine the upper limit breathing fatigue test data corresponding to the upper limit sample of the explosion-proof valve and the lower limit breathing fatigue test data corresponding to the lower limit sample of the explosion-proof valve; From the creep fatigue test data of the explosion-proof valve, determine the upper limit creep fatigue test data corresponding to the upper limit sample of the explosion-proof valve and the lower limit creep fatigue test data corresponding to the lower limit sample of the explosion-proof valve; Based on the upper limit respiratory fatigue test data and the upper limit creep fatigue test data, the upper limit valve opening pressure decay model is determined; Based on the lower limit respiratory fatigue test data and the lower limit creep fatigue test data, a lower limit valve opening pressure decay model is determined. The upper limit valve opening pressure decay model and the lower limit valve opening pressure decay model are determined as the explosion-proof valve decay model.

5. The method according to claim 4, characterized in that, Based on the upper limit respiratory fatigue experimental data and the upper limit creep fatigue experimental data, the upper limit valve opening pressure decay model is determined, including: Multiple test conditions were determined; Based on the multiple test conditions, data is extracted from the upper limit respiratory fatigue test data to obtain multiple first test data; The first decay model is obtained by fitting the multiple first experimental data. The first decay model is used to represent the mapping relationship between the valve opening pressure decay value and the number of cycles under different test conditions. Based on the multiple test conditions, data is extracted from the upper limit creep fatigue test data to obtain multiple second test data; The multiple second experimental data are fitted to obtain a second decay model, which is used to represent the mapping relationship between valve opening pressure decay value and time under different test conditions; The upper limit valve opening pressure decay model is determined based on the first decay model and the second decay model.

6. The method according to claim 5, characterized in that, The target operating conditions include daily mileage and fully charged mileage; the upper limit valve opening pressure decay model is determined based on the first decay model and the second decay model, including: The ratio of the daily driving mileage to the driving mileage on a full charge is determined as the equivalent number of daily cycles. Based on the daily equivalent cycle number, the first decay model is transformed to obtain a transformed decay model, which represents the mapping relationship between the valve opening pressure decay value and duration, temperature and breathing pressure. The conversion decay model is coupled with the second decay model to obtain the upper limit valve opening pressure decay model.

7. The method according to claim 1, characterized in that, The process of establishing the internal pressure model includes: Acquire internal pressure test data for the storage aging path and the cycle aging path of the battery under test; The internal air pressure experimental data of the storage aging path are fitted to obtain the first internal air pressure model; The internal air pressure experimental data of the cyclic aging path are fitted to obtain a second internal air pressure model; The internal pressure model is established based on the first internal pressure model and the second internal pressure model.

8. The method according to any one of claims 1-7, characterized in that, Inputting the target operating condition into the degradation model yields a degradation curve, including: Input the target operating condition into the upper limit valve opening pressure decay model to obtain the upper limit explosion-proof valve decay curve; Input the target operating condition into the lower limit valve opening pressure decay model to obtain the lower limit explosion-proof valve decay curve; The target working condition is input into the shell weld decay model to obtain the shell weld decay curve; The decay curves of the upper limit explosion-proof valve, the lower limit explosion-proof valve, and the shell weld are defined as the decay curves.

9. The method according to claim 8, characterized in that, The package life of the battery under test is determined based on the intersection of the degradation curve and the internal air pressure change curve, including: The intersection of the internal air pressure change curve and the lower limit explosion-proof valve decay curve is determined as the first intersection point, and the first abscissa value corresponding to the first intersection point is determined. The intersection of the decay curve of the shell weld and the decay curve of the upper limit explosion-proof valve is determined as the second intersection point, and the second abscissa value corresponding to the second intersection point is determined. The minimum value between the first horizontal coordinate value and the second horizontal coordinate value is determined as the package lifespan.

10. The method according to claim 9, characterized in that, After determining the package life of the battery under test, the process further includes: Collect the real-time operating parameters of the battery under test; Based on the real-time operating parameters, the decay model and the internal pressure model are corrected.

11. A device for predicting the lifespan of a battery package, characterized in that, include: The acquisition module is used to acquire breathing fatigue test data, creep fatigue test data and internal air pressure model of the battery under test in response to a prediction request for battery package life. The prediction request includes the target operating conditions of the battery under test. A module is established to build a degradation model for the battery under test based on the respiratory fatigue test data and the creep fatigue test data. The generation module is used to input the target operating condition into the decay model to obtain the decay curve, and to input the target operating condition into the internal pressure model to obtain the internal pressure change curve. The prediction module is used to determine the package life of the battery under test based on the intersection of the degradation curve and the internal air pressure change curve.

12. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-10.

13. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-10.

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