A battery explosion-proof performance test method and system based on big data

Abstract

The present invention discloses a battery explosion-proof performance test method and system based on big data, which relates to the technical field of battery explosion-proof performance test. The battery explosion-proof performance test method comprises the following steps: using structural analysis technology and test instruments to build a test platform, and based on a preset heat source test plan, testing and recording the reaction process of the battery through the test platform; using the reaction process to evaluate the response time of the battery to generate thermal runaway under heat source conditions, and constructing an evaluation model based on the response time and energy transfer technology to determine the damage degree value of the battery; comparing the damage degree value with the preset damage value, setting the battery less than the preset damage value as a preliminary qualified product, and setting the battery greater than the preset damage value as a non-qualified product for elimination; using virtual excitation to implement vibration response analysis, and obtaining the explosion-proof performance of the battery under vibration based on the response analysis result. The present invention helps to improve the accuracy and efficiency of the evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of battery explosion-proof performance testing, and in particular to a battery explosion-proof performance testing method and system based on big data. Background Art

[0002] A battery is a device that converts chemical energy, light energy, atomic energy or other forms of energy into electrical energy. Batteries are mainly divided into primary batteries (dry batteries) and secondary batteries (rechargeable batteries). With the development of technology, batteries have gone through different stages from the initial voltaic batteries to nickel-cadmium batteries, nickel-metal hydride batteries, lithium batteries and the developing fuel cells. Battery explosions are usually caused by abnormal conditions during charging or use, such as overcharging or over-discharging, short circuit or overheating.

[0003] Battery explosion may cause serious personal injury or even endanger life, and may also damage surrounding equipment or facilities, causing property loss, etc. At the same time, battery explosion may release toxic and harmful substances and pollute the environment. In order to avoid battery explosion, existing manufacturers will choose materials that are stable and non-flammable at high temperatures to make battery components, and conduct strict quality control and testing during the battery production process to ensure the quality and safety of the battery.

[0004] However, in the prior art, when conducting explosion-proof performance tests on batteries, the condition of the battery under vibration is often ignored. Under a vibration environment, the internal components of the battery may be displaced or loosened, increasing the risk of short circuit or thermal runaway, thereby affecting the explosion-proof performance of the battery, resulting in an incomplete assessment of the performance and safety of the battery in a real working environment, affecting the explosion-proof performance of the battery. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a battery explosion-proof performance test method and system based on big data, so as to achieve the purpose of improving the accuracy and efficiency of evaluating battery explosion-proof performance.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a battery explosion-proof performance test method based on big data, and the battery explosion-proof performance test method comprises the following steps:

[0008] S1. Use structural analysis technology and test instruments to build a test platform, and test and record the battery reaction process through the test platform based on a pre-set heat source test plan;

[0009] S2. Use the reaction process to evaluate the response time of the battery to thermal runaway under heat source conditions, and build an evaluation model based on the response time and energy transfer technology to determine the degree of damage to the battery;

[0010] S3, comparing the damage degree value with a preset damage value, setting batteries with a damage value less than the preset damage value as preliminary qualified products, and setting batteries with a damage value greater than the preset damage value as unqualified products for elimination;

[0011] S4. Use the virtual excitation method to perform vibration response analysis on the preliminary qualified products, and build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration.

[0012] Furthermore, a test platform is built using structural analysis technology and test instruments, and the reaction process of the battery is tested and recorded through the test platform based on a pre-set heat source test plan, including the following steps:

[0013] S11. Using structural analysis technology to establish a three-dimensional experimental cabin of preset size according to the structural parameter information of the battery, and integrating a temperature sensor, a data collector, a digital camera and a heater in the three-dimensional experimental cabin to obtain a test platform;

[0014] S12, constructing a battery model in the test platform and equidistantly arranging heaters on the surface of the battery model, and setting the heating temperature, heating frequency and duration of the heaters based on a pre-set heat source test plan;

[0015] S13, using temperature sensors to record the surface temperatures of the top, middle and bottom of the battery, and defining the recording frequencies of the data collector and the digital camera according to the heat source test plan to obtain battery status data;

[0016] S14. Combining the battery surface temperature, battery status data and heat conduction technology to analyze the changes in the battery under the influence of the heater to obtain the battery reaction process.

[0017] Furthermore, the reaction process is used to evaluate the response time of the battery to generate thermal runaway under heat source conditions, and an evaluation model is constructed based on the response time and energy transfer technology to determine the degree of damage to the battery, including the following steps:

[0018] S21, dividing the movement area of ​​the internal temperature of the battery according to the reaction process to obtain the battery change speed, and performing Taylor expansion operation on the heating frequency at a preset time based on a discrete time series to obtain the battery state equation;

[0019] S22, determining the distribution position of the temperature at the battery according to the reaction process, and combining the distribution position with the battery change speed to generate a battery observation equation;

[0020] S23, combining the battery state equation with the battery observation equation and using particle filtering technology to obtain a state particle set estimating a response time when the battery generates thermal runaway at a preset time;

[0021] S24. Analyze the response characteristics under heat source conditions based on the response time, and use the response characteristics as the basis to build an evaluation model to determine the battery's tolerance and evaluate the degree of damage to the battery.

[0022] Furthermore, the expressions of the battery state equation and the battery observation equation are:

[0023] ;

[0024] In the formula, X k Indicated in k The battery status at all times; Z k Indicated in k The observed value of the battery at all times; F k Indicated in k The heating frequency of the heater at all times; U k Indicated in k The heating time of the heater; T k Indicated in k Thermal conductivity at the time; H k Indicated in k The distribution point of temperature in the battery at each moment; R k Indicated in k The battery changes speed at all times.

[0025] Furthermore, combining the battery state equation with the battery observation equation and using particle filtering technology to obtain the state particle set estimation response time of the battery generating thermal runaway at a preset time includes the following steps:

[0026] S231, setting heating time based on particle filtering technology k , generate initial particles and particle weights to complete the initialization parameter setting, and obtain the battery state equation k The predicted value of the state at the moment;

[0027] S232, obtain according to the battery temperature distribution result k The internal temperature value of the battery at the moment, and the characteristic relationship between temperature and heating frequency is established to reflect the battery k The changing laws of time;

[0028] S233, based on the law of change, the initial particle weights are normalized and resampled to generate new particle estimates k The posterior probability distribution of the battery producing thermal runaway at the moment is calculated, and the response time of the battery when thermal runaway occurs is estimated by the posterior probability distribution result.

[0029] Further, based on the response time analysis of the response characteristics under the heat source condition, and taking the response characteristics as the basis to construct an evaluation model to judge the tolerance reached by the battery, the evaluation of the damage degree value of the battery includes the following steps:

[0030] S241, analyzing the battery state change response relationship according to the relationship between the response time analysis and the heating frequency and temperature setting, and fitting the response relationship to describe the response characteristics using an adaptive optimal segmentation method;

[0031] S242, determining the fluctuation of the battery's tolerance during the thermal runaway process within the response time according to the response characteristics, and constructing a response characteristic matrix based on the fluctuation and the response characteristics;

[0032] S243, using the response characteristic matrix as an adoption condition combined with the scenario generation method to simulate the fluctuation of bearing capacity, and using the fluctuation results as the basis to construct the assessment model;

[0033] S234. Evaluate the degree of damage to the battery by predicting the maximum tolerance of the battery under heat source conditions based on the evaluation model.

[0034] Furthermore, the virtual excitation method is used to perform vibration response analysis on the preliminary qualified products, and a stress analysis model is built based on the response analysis results to obtain the explosion-proof performance of the battery under vibration, including the following steps:

[0035] S41, select preliminary qualified products as secondary test objects, and set vibration test parameters, the test parameters include vibration frequency, vibration amplitude and vibration duration;

[0036] S42, performing modal analysis on the components according to the actual assembly relationship of the secondary test object, and establishing a vibration test platform using a virtual excitation method and finite element general software based on the modal analysis results;

[0037] S43, simulating and recording the vibration response of the secondary test object at the vibration frequency under the set vibration test parameter conditions in the vibration test platform;

[0038] S44, extracting vibration characteristics based on vibration response data and combining them with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions;

[0039] S45. Analyze the structural integrity of the secondary test object under the vibration test parameter setting based on the stress distribution results, and use the integrity analysis results to determine the explosion-proof performance of the battery.

[0040] Furthermore, extracting vibration characteristics based on vibration response data and combining with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions includes the following steps:

[0041] S441, identifying the resonant frequency of the secondary test object according to the vibration response data as a vibration feature, and collecting the elastic modulus, Poisson's ratio, and yield strength of the secondary test object as material properties;

[0042] S442. Use finite element analysis software to establish a stress analysis model based on vibration characteristics and material properties to obtain the maximum stress when component displacement and cavity gap change occur;

[0043] S443, verifying the stress analysis model based on the maximum stress, and setting acceleration response change conditions in the stress analysis model to obtain several groups of maximum stress distribution conditions;

[0044] S444. Analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions based on the maximum stress value combined with support vector machine.

[0045] Further, analyzing the stress distribution values ​​of each component of the secondary test object under vibration conditions based on the maximum stress value combined with the support vector machine includes the following steps:

[0046] S4441. Obtaining the location of a stress concentration area based on several groups of maximum stress distribution conditions, and determining the corresponding vibration conditions within the area location;

[0047] S4442, obtaining vibration conditions corresponding to the maximum stress distribution of the selected preset component, performing data standardization and dimensionality reduction processing as a training set;

[0048] S4443. Substitute the training set into the support vector machine for training, and use the trained support vector machine to analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions.

[0049] In a second aspect, the present invention further provides a battery explosion-proof performance test system based on big data, the battery explosion-proof performance test system comprising a test platform building module, a damage degree judgment module, a comparison and rejection setting module and an explosion-proof performance test module, wherein the test platform building module, the damage degree judgment module, the comparison and rejection setting module and the explosion-proof performance test module are connected in sequence;

[0050] The test platform building module is used to build a test platform using structural analysis technology and test instruments, and test and record the battery reaction process through the test platform based on a pre-set heat source test plan;

[0051] The damage degree judgment module is used to evaluate the response time of the battery to thermal runaway under heat source conditions by using the reaction process, and to build an evaluation model based on the response time and energy transfer technology to determine the damage degree value of the battery;

[0052] A comparison and rejection setting module is used to compare the damage degree value with the preset damage value, set the battery with a damage value less than the preset value as a preliminary qualified product, and set the battery with a damage value greater than the preset value as a unqualified product for rejection;

[0053] The explosion-proof performance test module is used to perform vibration response analysis on preliminary qualified products using the virtual excitation method, and to build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration.

[0054] The beneficial effects of the present invention are:

[0055] 1. The present invention comprehensively evaluates the performance of the battery under extreme conditions by combining heat source test, response time analysis and vibration response analysis, provides multi-dimensional data on the explosion-proof performance of the battery, and screens out batteries that meet safety requirements by setting a preset damage value as the standard for battery qualification, ensuring that only batteries that perform well in thermal runaway and vibration tests can be identified as qualified products. In addition, the present invention uses advanced data analysis technology to accurately evaluate battery performance, which helps to improve the accuracy and efficiency of the evaluation, continuously optimize the design of the battery, and improve its structural stability and explosion-proof performance.

[0056] 2. The present invention utilizes structural analysis technology and testing instruments to build a test platform. By integrating temperature sensors, data collectors, and digital cameras, it is possible to comprehensively collect battery status data from multiple dimensions, providing rich information for subsequent analysis of battery performance and safety, making the analysis results more comprehensive and accurate. In addition, by combining battery surface temperature and battery status data with heat conduction technology analysis, it is possible to deeply understand the internal change mechanism of the battery during the heating process, which is helpful to identify potential safety hazards in battery design.

[0057] 3. The present invention obtains the battery change rate by implementing a partitioning operation so as to more accurately track the dynamic changes of the internal temperature of the battery, and uses the Taylor expansion operation to obtain the battery state equation, which can more accurately describe the state change of the battery when the heating frequency changes, and judge the distribution position of the temperature at the battery in combination with the change rate, so as to more comprehensively understand the reaction of the battery under heat source conditions, and at the same time use particle filtering technology to obtain the state particle set of the battery producing thermal runaway, which can more effectively deal with nonlinear and non-Gaussian problems, and can more accurately predict the behavior of the battery under heat source conditions. By building an evaluation model to determine the degree of damage of the battery, a scientific basis is provided for the design, use and safety evaluation of the battery.

[0058] 4. The present invention performs vibration response analysis on battery products through virtual excitation method and finite element analysis, and establishes a stress analysis model based on the analysis to evaluate the explosion-proof performance of the battery. The structural integrity of the battery is analyzed using the stress distribution results, which can comprehensively judge the safety of the battery under specific vibration conditions, help evaluate the explosion-proof performance of the battery, and ensure its reliability and safety in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0060] Figure 1 is a flow chart of a battery explosion-proof performance test method based on big data according to an embodiment of the present invention;

[0061] Figure 2 The figure is a principle block diagram of a battery explosion-proof performance test system based on big data according to an embodiment of the present invention.

[0062] In the figure:

[0063] 1. Test platform building module; 2. Destruction degree judgment module; 3. Comparison and elimination setting module; 4. Explosion-proof performance test module. DETAILED DESCRIPTION

[0064] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0065] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0066] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that the terms "include" and "have" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0067] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0068] See also Figure 1The present invention provides a battery explosion-proof performance test method based on big data, and the battery explosion-proof performance test method comprises the following steps:

[0069] S1. Use structural analysis technology and testing instruments to build a test platform, and test and record the battery reaction process through the test platform based on the pre-set heat source test plan.

[0070] In this embodiment, a test platform is built using structural analysis technology and test instruments, and the reaction process of the battery is tested and recorded through the test platform based on a pre-set heat source test plan, including the following steps:

[0071] S11. A three-dimensional experimental chamber of preset dimensions is established using structural analysis technology according to the structural parameter information of the battery, and a temperature sensor, a data collector, a digital camera and a heater are integrated in the three-dimensional experimental chamber to obtain a test platform.

[0072] Specifically, a three-dimensional experimental chamber of preset size is established using structural analysis technology according to the structural parameter information of the battery, and a temperature sensor, a data collector, a digital camera and a heater are integrated in the three-dimensional experimental chamber to obtain a test platform, which includes the following steps:

[0073] Collect and analyze the structural parameter information of the battery, such as the size, shape, material properties, etc. of the battery, determine the impact of these structural parameters on the design of the experimental cabin, especially the requirements for size and shape, and use structural analysis technology and modeling software to design a three-dimensional experimental cabin of preset size based on the structural parameter information of the battery. Note that the size of the experimental cabin is smaller than the size of the battery.

[0074] Select temperature sensors, data loggers, digital cameras and heaters, and arrange the positions of various devices according to the layout of the experimental cabin to ensure that the temperature sensor can accurately monitor the temperature changes of the battery and the camera can clearly record the experimental process. Install temperature sensors, data loggers, digital cameras, heaters and other devices in the experimental cabin, and ensure that all devices are correctly connected and configured to obtain the test platform.

[0075] S12. Construct a battery model in the test platform and arrange heaters equidistantly on the surface of the battery model, and set the heater heating temperature, heating frequency and duration based on a pre-set heat source test plan.

[0076] Specifically, a battery model is constructed in the test platform and heaters are arranged equidistantly on the surface of the battery model, and the heating temperature, heating frequency and duration of the heaters are set based on a pre-set heat source test plan, including the following steps:

[0077] According to the structural parameters and material properties of the actual battery, use appropriate modeling tools or software to build a battery model to ensure that the model accurately reflects the battery's geometric dimensions, material properties and internal structure. The battery model is deployed in the experimental platform, and the position should be optimized according to the experimental requirements to ensure the accuracy of the test results.

[0078] Heaters should be arranged at equal distances on the surface of the battery model. The layout plan should take into account the uniformity of heating and the specific needs of the experiment. The selection and layout of heaters should ensure that they can cover all or specific parts of the battery model to simulate different heating scenarios. A detailed heat source test plan should be developed, including the setting of heating temperature, heating frequency (such as periodic heating) and heating time. According to the test plan, the control parameters of the heater should be set, including heating temperature, heating frequency and heating time.

[0079] S13. Use temperature sensors to record the surface temperatures of the top, middle and bottom of the battery, and define the recording frequencies of the data logger and the digital camera according to the heat source test plan to obtain battery status data.

[0080] Specifically, using temperature sensors to record the surface temperatures of the top, middle and bottom of the battery, and defining the recording frequency of the data collector and the digital camera according to the heat source test plan to obtain the battery status data includes the following steps:

[0081] Select the appropriate type of temperature sensor according to the experimental requirements, ensure that the temperature sensor can cover the measurement points at the top, middle and bottom of the battery, arrange the temperature sensors at equal distances at the top, middle and bottom of the battery, and ensure that the sensor is in good contact with the battery surface to accurately record the temperature data.

[0082] Connect the temperature sensor to the data logger and configure the data logger to collect data according to the test plan, including setting the appropriate recording frequency. According to the heat source test plan, set the recording frequency of the digital camera to ensure that the state changes of the battery during the heating process can be captured.

[0083] Before officially starting the test, conduct a pre-test to verify that the temperature sensors, data loggers, and digital cameras are configured correctly and that they can work together to record the required data. According to the heat source test plan, start the heater and begin recording data. Monitor the data logger and digital camera to ensure that they record data and images correctly at the set frequency.

[0084] S14. Combining the battery surface temperature, battery status data and heat conduction technology to analyze the changes in the battery under the influence of the heater to obtain the battery reaction process.

[0085] Specifically, the battery surface temperature, battery status data and heat conduction technology are combined to analyze the changes of the battery under the influence of the heater to obtain the battery reaction process, which includes the following steps:

[0086] Based on the surface temperature data of the top, middle and bottom of the battery at different time points, as well as the battery status data, these data are integrated to form a time series of the temperature change and state change of the battery during the entire heating process. Based on the structural parameters and material properties of the battery, a heat conduction model is established to describe how heat propagates inside the battery and on its surface.

[0087] The heat conduction equation, such as Fourier's law of heat conduction, is used to simulate the spatial distribution and temporal evolution of the battery temperature when heated by the heater. The established heat conduction model is used to input parameters such as the heating temperature, frequency, and duration of the heater in the experiment to simulate the temperature distribution inside and on the surface of the battery during the heating process. The changing trend of the battery temperature under heating conditions and the possible impact of these changes on battery performance and safety are analyzed.

[0088] Therefore, a test platform is built using structural analysis technology and testing instruments. By integrating temperature sensors, data loggers and digital cameras, it is possible to collect battery status data from multiple dimensions, providing rich information for subsequent analysis of battery performance and safety, making the analysis results more comprehensive and accurate.

[0089] S2. Use the reaction process to evaluate the response time of the battery to thermal runaway under heat source conditions, and build an evaluation model based on the response time and energy transfer technology to determine the degree of battery damage.

[0090] In this embodiment, the reaction process is used to evaluate the response time of the battery to generate thermal runaway under heat source conditions, and an evaluation model is constructed based on the response time and energy transfer technology to determine the damage degree of the battery, including the following steps:

[0091] S21. According to the reaction process, the movement area of ​​the internal temperature of the battery is divided to obtain the battery change speed, and the heating frequency at a preset time is subjected to Taylor expansion operation based on a discrete time series to obtain the battery state equation.

[0092] Specifically, the movement area of ​​the internal temperature of the battery is divided according to the reaction process to obtain the battery change speed, and the heating frequency at a preset time is subjected to Taylor expansion operation based on a discrete time series to obtain the battery state equation, including the following steps:

[0093] Use temperature sensors to collect temperature data inside the battery under different heating conditions, and analyze changes in temperature distribution inside the battery based on the collected temperature data. Divide the inside of the battery into different temperature movement zones based on the speed and direction of temperature change.

[0094] For each divided temperature movement area, the speed of temperature change over time is calculated, and the continuous temperature data is converted into a discrete time series in order to perform Taylor expansion operation on the heating frequency and select appropriate time intervals to discretize the data to ensure that the dynamic changes in temperature can be accurately captured.

[0095] For each preset heating moment, a Taylor expansion operation is performed on the heating frequency based on a discrete time series. Combining the Taylor expansion results of the battery change rate and the heating frequency, a mathematical model describing the change of the battery state over time, namely the battery state equation, is established.

[0096] S22. Determine the temperature distribution position at the battery according to the reaction process, and combine the distribution position with the battery change speed to generate a battery observation equation.

[0097] Specifically, judging the distribution position of the temperature at the battery according to the reaction process, and combining the distribution position with the battery change speed to generate the battery observation equation includes the following steps:

[0098] Use the data collected by the temperature sensor to analyze the internal and surface temperature distribution of the battery under different operating conditions. Through data analysis, determine the temperature distribution in various parts of the battery, identify high and low temperature areas, and based on the temperature distribution analysis results, determine the specific distribution location of the temperature inside and on the surface of the battery.

[0099] The battery is divided into several regions, each of which is identified according to its temperature range, temperature gradient, and change rate. For each region, the rate of temperature change over time is calculated by taking the time derivative or difference of the continuous measurement data.

[0100] Combining the information of temperature distribution location and battery change rate, a battery observation equation is established. This equation should be able to describe the real-time status of the battery, including temperature distribution and temperature change rate, as the basis for battery health status and safety assessment.

[0101] Among them, the expressions of the battery state equation and the battery observation equation are:

[0102] ;

[0103] In the formula, X k Indicated in k The battery status at all times; Z k Indicated in k The observed value of the battery at all times; F k Indicated in k The heating frequency of the heater at all times; Uk Indicated in k The heating time of the heater; T k Indicated in k Thermal conductivity at the time; H k Indicated in k The distribution point of temperature in the battery at each moment; R k Indicated in k The battery changes speed at all times.

[0104] S23. Combining the battery state equation with the battery observation equation, using particle filtering technology to obtain a state particle set to estimate the response time when the battery produces thermal runaway at a preset time.

[0105] Specifically, the battery state equation is combined with the battery observation equation and the particle filtering technology is used to obtain the state particle set estimation response time of the battery generating thermal runaway at a preset time, which includes the following steps:

[0106] S231, setting heating time based on particle filtering technology k , generate initial particles and particle weights to complete the initialization parameter setting, and obtain the battery state equation k The predicted value of the state at the moment;

[0107] S232, obtain according to the battery temperature distribution result k The internal temperature value of the battery at the moment, and the characteristic relationship between temperature and heating frequency is established to reflect the battery k The changing laws of time;

[0108] S233, based on the law of change, the initial particle weights are normalized and resampled to generate new particle estimates k The posterior probability distribution of the battery producing thermal runaway at the moment is calculated, and the response time of the battery when thermal runaway occurs is estimated by the posterior probability distribution result.

[0109] S24. Analyze the response characteristics under heat source conditions based on the response time, and use the response characteristics as the basis to build an evaluation model to determine the battery's tolerance and evaluate the degree of damage to the battery.

[0110] Specifically, based on the response time analysis of the response characteristics under the heat source condition, and using the response characteristics as the basis to construct an evaluation model to determine the battery's tolerance and evaluate the battery's damage degree value, the following steps are included:

[0111] S241, analyzing the battery state change response relationship according to the relationship between the response time analysis and the heating frequency and temperature setting, and fitting the response relationship to describe the response characteristics using an adaptive optimal segmentation method;

[0112] S242, determining the fluctuation of the battery's tolerance during the thermal runaway process within the response time according to the response characteristics, and constructing a response characteristic matrix based on the fluctuation and the response characteristics;

[0113] S243, using the response characteristic matrix as an adoption condition combined with the scenario generation method to simulate the fluctuation of bearing capacity, and using the fluctuation results as the basis to construct the assessment model;

[0114] S234. Evaluate the degree of damage to the battery by predicting the maximum tolerance of the battery under heat source conditions based on the evaluation model.

[0115] Therefore, by implementing the division operation to obtain the battery change rate, the dynamic changes of the internal temperature of the battery can be tracked more accurately, and the battery state equation can be obtained by using the Taylor expansion operation, which can more accurately describe the state changes of the battery when the heating frequency changes. By combining the distribution position of the temperature at the battery with the change rate, a more comprehensive understanding of the battery's reaction under heat source conditions can be achieved.

[0116] S3. Compare the damage degree value with the preset damage value, set the batteries with damage value less than the preset damage value as preliminary qualified products, and set the batteries with damage value greater than the preset damage value as unqualified products for elimination.

[0117] In this embodiment, comparing the damage degree value with the preset damage value, setting the battery with a damage value less than the preset damage value as a preliminary qualified product, and setting the battery with a damage value greater than the preset damage value as a unqualified product for elimination includes the following steps:

[0118] According to the application scenarios and safety requirements of the battery, the preset damage value threshold defines the maximum acceptable degree of damage to the battery. The setting of the damage value needs to be based on sufficient experimental data and statistical analysis to ensure that it is both reasonable and practical.

[0119] The damage degree value of each battery is compared with the preset damage value. If the damage degree value of the battery is less than or equal to the preset damage value, the battery is classified as a preliminary qualified product; if it is greater than the preset damage value, it is judged as a failed product.

[0120] Therefore, the rejection step can effectively classify the batteries into preliminary qualified batteries and unqualified batteries, ensuring that only batteries that meet the quality standards will be further used or sold, thereby improving the reliability and safety of the product.

[0121] S4. Use the virtual excitation method to perform vibration response analysis on the preliminary qualified products, and build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration.

[0122] In this embodiment, a virtual excitation method is used to perform vibration response analysis on preliminary qualified products, and a stress analysis model is built based on the response analysis results to obtain the explosion-proof performance of the battery under vibration, including the following steps:

[0123] S41. Select preliminary qualified products as secondary test objects and set vibration test parameters, which include vibration frequency, vibration amplitude and vibration duration.

[0124] Specifically, selecting preliminary qualified products as secondary test objects and setting vibration test parameters includes the following steps:

[0125] From the batteries that have passed the initial quality control test, a certain number of preliminary qualified products are selected as the objects of secondary testing. According to the application environment and safety requirements of the batteries, the specific parameters of the vibration test are set, including vibration frequency, vibration amplitude and vibration duration.

[0126] S42. Perform modal analysis on the components according to the actual assembly relationship of the secondary test object, and establish a vibration test platform based on the modal analysis results using the virtual excitation method and finite element general software.

[0127] Specifically, the modal analysis of the components is performed according to the actual assembly relationship of the secondary test object, and a vibration test platform is established based on the modal analysis results using a virtual excitation method and finite element general software, including the following steps:

[0128] Collect detailed assembly drawings and related parameters of the secondary test objects (i.e., preliminarily qualified batteries and their components) to ensure that their geometric structure, material properties, connection methods, etc. are understood; use finite element analysis (FEA) software to establish an accurate geometric model based on the actual assembly relationship of the battery and its components, define material properties, boundary conditions, and contact relationships, and ensure that the model can truly reflect the actual physical state.

[0129] Perform modal analysis in finite element software to identify the natural frequencies and mode shapes of the battery and its components when subjected to vibration. The results should reveal the dynamic response characteristics of the battery and components, including possible resonant frequencies.

[0130] Study the results of modal analysis, especially focus on the main vibration modes and natural frequencies of batteries and components, identify possible structural weaknesses or resonance problem areas, and provide a basis for the design of vibration test platforms.

[0131] Using the modal analysis results, the vibration environment is simulated in the finite element software using virtual excitation methods (such as applying imaginary external forces or displacements) to design the structure of the vibration test platform to ensure that appropriate vibrations can be applied at critical frequencies to verify the vibration response of the battery and its components.

[0132] S43. In the vibration test platform, simulate and record the vibration response of the secondary test object at the vibration frequency according to the set vibration test parameter conditions. The vibration response includes acceleration response, component displacement, and cavity gap.

[0133] Specifically, simulating and recording the vibration response of the secondary test object at the vibration frequency under the set vibration test parameter conditions in the vibration test platform includes the following steps:

[0134] According to the test design and requirements, the vibration parameters of the vibration test platform are accurately set, including the vibration frequency, vibration amplitude and vibration duration, and the secondary test objects (i.e., the battery and its components) are fixed on the vibration test platform according to the actual assembly relationship.

[0135] According to the type of vibration response that needs to be recorded (such as acceleration response, component displacement, cavity gap), arrange the corresponding sensors and measuring equipment on the test object and in the test platform, start the vibration test platform, and conduct vibration tests according to the preset parameter conditions.

[0136] The data logger is used to record the vibration response data in real time, including acceleration response, component displacement, cavity gap and other information.

[0137] S44. Extract vibration characteristics based on vibration response data and combine with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions.

[0138] Specifically, extracting vibration characteristics based on vibration response data and combining with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions includes the following steps:

[0139] S441, identifying the resonant frequency of the secondary test object according to the vibration response data as a vibration feature, and collecting the elastic modulus, Poisson's ratio, and yield strength of the secondary test object as material properties;

[0140] S442. Use finite element analysis software to establish a stress analysis model based on vibration characteristics and material properties to obtain the maximum stress when component displacement and cavity gap change occur;

[0141] S443, verifying the stress analysis model based on the maximum stress, and setting acceleration response change conditions in the stress analysis model to obtain several groups of maximum stress distribution conditions;

[0142] S444. Analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions based on the maximum stress value combined with support vector machine.

[0143] Among them, analyzing the stress distribution values ​​of each component of the secondary test object under vibration conditions based on the maximum stress value combined with the support vector machine includes the following steps:

[0144] S4441. Obtaining the location of a stress concentration area based on several groups of maximum stress distribution conditions, and determining the corresponding vibration conditions within the area location;

[0145] S4442, obtaining vibration conditions corresponding to the maximum stress distribution of the selected preset component, performing data standardization and dimensionality reduction processing as a training set;

[0146] S4443. Substitute the training set into the support vector machine for training, and use the trained support vector machine to analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions.

[0147] S45. Analyze the structural integrity of the secondary test object under the vibration test parameter setting based on the stress distribution results, and use the integrity analysis results to determine the explosion-proof performance of the battery.

[0148] Specifically, analyzing the structural integrity of the secondary test object under the vibration test parameter setting based on the stress distribution results, and using the integrity analysis results to determine the explosion-proof performance of the battery includes the following steps:

[0149] Use stress analysis tools and techniques (such as strain gauges, finite element analysis software, etc.) to collect stress distribution data of the secondary test object after the vibration test, ensuring that the data covers the key areas of the battery and its components to fully understand the stress distribution.

[0150] The collected stress distribution data is analyzed in detail to identify stress concentration areas and possible weak points, analyze the stress levels of various components and structures, compare them with the allowable stress or fatigue limit of the material, and evaluate the safety margin of the structure.

[0151] Based on the stress distribution analysis results, the structural integrity of the secondary test object is evaluated, the influence of vibration test parameters (frequency, amplitude, duration, etc.) on the structural integrity is considered, and the reliability of the structure under actual use conditions is judged.

[0152] Identify the structural weaknesses of batteries and their components, as well as potential modes that may lead to structural failure, such as crack formation, loose or broken joints, etc., analyze the impact of these weaknesses in practical applications, and the potential risks to battery performance and safety, and evaluate the explosion-proof performance of the battery by combining the structural integrity analysis and weak point identification results.

[0153] Therefore, the vibration response analysis of battery products is carried out through virtual excitation method and finite element analysis, and a stress analysis model is established based on the analysis to evaluate the explosion-proof performance of the battery. The structural integrity of the battery is analyzed using the stress distribution results. The safety of the battery under specific vibration conditions can be comprehensively judged, which helps to evaluate the explosion-proof performance of the battery and ensure its reliability and safety in practical applications.

[0154] See also Figure 2 The present invention also provides a battery explosion-proof performance test system based on big data, the battery explosion-proof performance test system comprises a test platform building module 1, a damage degree judgment module 2, a comparison and rejection setting module 3 and an explosion-proof performance test module 4, wherein the test platform building module 1, the damage degree judgment module 2, the comparison and rejection setting module 3 and the explosion-proof performance test module 4 are connected in sequence;

[0155] Test platform building module 1, used to build a test platform using structural analysis technology and test instruments, and test and record the reaction process of the battery through the test platform based on a pre-set heat source test plan;

[0156] The damage degree judgment module 2 is used to evaluate the response time of the battery to generate thermal runaway under heat source conditions by using the reaction process, and to build an evaluation model based on the response time and energy transfer technology to determine the damage degree value of the battery;

[0157] The comparison and rejection setting module 3 is used to compare the damage degree value with the preset damage value, set the battery with a damage value less than the preset value as a preliminary qualified product, and set the battery with a damage value greater than the preset value as a unqualified product for rejection;

[0158] The explosion-proof performance test module 4 is used to perform vibration response analysis on preliminary qualified products using a virtual excitation method, and to build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration.

[0159] In summary, with the help of the above technical solutions of the present invention, the present invention comprehensively evaluates the performance of the battery under extreme conditions by combining heat source test, response time analysis and vibration response analysis, provides multi-dimensional data on the explosion-proof performance of the battery, and sets a preset damage value as the standard for battery qualification to screen out batteries that meet safety requirements, ensuring that only batteries that perform well in thermal runaway and vibration tests can be identified as qualified products, and uses advanced data analysis technology to accurately evaluate battery performance, which helps to improve the accuracy and efficiency of the evaluation, continuously optimize the design of the battery, and improve its structural stability and explosion-proof performance. The present invention uses structural analysis technology and test instruments to build a test platform, and integrates temperature sensors, data collectors and digital cameras to comprehensively collect battery status data from multiple dimensions, providing rich information for later analysis of battery performance and safety, making the analysis results more comprehensive and accurate, and by combining battery surface temperature and battery status data with heat conduction technology analysis, it is possible to deeply understand the internal change mechanism of the battery during the heating process, which helps to identify potential safety hazards in battery design.

[0160] The present invention obtains the battery change speed by implementing the division operation so as to more accurately track the dynamic change of the internal temperature of the battery, and uses the Taylor expansion operation to obtain the battery state equation, which can more accurately describe the state change of the battery when the heating frequency changes, and judge the distribution position of the temperature at the battery in combination with the change speed, so as to more comprehensively understand the reaction of the battery under the heat source condition, and at the same time use the particle filtering technology to obtain the state particle set of the battery generating thermal runaway, which can more effectively deal with nonlinear and non-Gaussian problems, and can more accurately predict the behavior of the battery under the heat source condition, and use the construction of the evaluation model to judge the degree of damage of the battery, so as to provide a scientific basis for the design, use and safety evaluation of the battery. The present invention performs vibration response analysis on the battery product through the virtual excitation method and finite element analysis, and establishes a stress analysis model based on the analysis to evaluate the explosion-proof performance of the battery, and uses the stress distribution results to analyze the structural integrity of the battery, so as to comprehensively judge the safety of the battery under specific vibration conditions, which is helpful to evaluate the explosion-proof performance of the battery and ensure its reliability and safety in practical applications.

[0161] Those skilled in the art will appreciate that the units, i.e., algorithm steps, of the various examples described in conjunction with this embodiment can be implemented in electronic hardware or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0162] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A battery explosion-proof performance test method based on big data, characterized in that: The battery explosion-proof performance test method comprises the following steps: S1. Use structural analysis technology and test instruments to build a test platform, and test and record the battery reaction process through the test platform based on a pre-set heat source test plan; S2. Use the reaction process to evaluate the response time of the battery to thermal runaway under heat source conditions, and build an evaluation model based on the response time and energy transfer technology to determine the degree of damage to the battery; S3, comparing the damage degree value with a preset damage value, setting batteries with a damage value less than the preset damage value as preliminary qualified products, and setting batteries with a damage value greater than the preset damage value as unqualified products for elimination; S4. Use virtual excitation method to perform vibration response analysis on preliminary qualified products, and build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration; The method of evaluating the response time of a battery to generate thermal runaway under heat source conditions by using a reaction process, and constructing an evaluation model based on the response time and energy transfer technology to determine the degree of damage to the battery includes the following steps: S21, dividing the movement area of ​​the internal temperature of the battery according to the reaction process to obtain the battery change speed, and performing Taylor expansion operation on the heating frequency at a preset time based on a discrete time series to obtain the battery state equation; S22, determining the distribution position of the temperature at the battery according to the reaction process, and combining the distribution position with the battery change speed to generate a battery observation equation; S23, combining the battery state equation with the battery observation equation and using particle filtering technology to obtain a state particle set estimating a response time when the battery generates thermal runaway at a preset time; S24, analyzing the response characteristics under heat source conditions based on the response time, and using the response characteristics as a basis to construct an evaluation model to determine the battery's tolerance and evaluate the degree of damage to the battery; The method of using the virtual excitation method to perform vibration response analysis on preliminary qualified products, and building a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration conditions includes the following steps: S41, selecting preliminary qualified products as secondary test objects, and setting vibration test parameters, wherein the test parameters include vibration frequency, vibration amplitude and vibration duration; S42, performing modal analysis on the components according to the actual assembly relationship of the secondary test object, and establishing a vibration test platform using a virtual excitation method and finite element general software based on the modal analysis results; S43, simulating and recording the vibration response of the secondary test object at the vibration frequency under the set vibration test parameter conditions in the vibration test platform; S44, extracting vibration characteristics based on vibration response data and combining them with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions; S45. Analyze the structural integrity of the secondary test object under the vibration test parameter setting based on the stress distribution results, and use the integrity analysis results to determine the explosion-proof performance of the battery.

2. A battery explosion-proof performance test method based on big data according to claim 1, characterized in that: The method of using structural analysis technology and testing instruments to build a test platform, and testing and recording the reaction process of the battery through the test platform based on a pre-set heat source test plan includes the following steps: S11. Using structural analysis technology to establish a three-dimensional experimental cabin of preset size according to the structural parameter information of the battery, and integrating a temperature sensor, a data collector, a digital camera and a heater in the three-dimensional experimental cabin to obtain a test platform; S12, constructing a battery model in the test platform and equidistantly arranging heaters on the surface of the battery model, and setting the heating temperature, heating frequency and duration of the heaters based on a pre-set heat source test plan; S13, using temperature sensors to record the surface temperatures of the top, middle and bottom of the battery, and defining the recording frequencies of the data collector and the digital camera according to the heat source test plan to obtain battery status data; S14. Combining the battery surface temperature, battery status data and heat conduction technology to analyze the changes in the battery under the influence of the heater to obtain the battery reaction process.

3. A battery explosion-proof performance test method based on big data according to claim 1, characterized in that: The expressions of the battery state equation and the battery observation equation are: ； In the formula, X k Indicated in k The battery status at all times; Z k Indicated in k The observed value of the battery at all times; F k Indicated in k The heating frequency of the heater at all times; U k Indicated in k The heating time of the heater; T k Indicated in k Thermal conductivity at the time; H k Indicated in k The distribution point of temperature in the battery at each moment; R k Indicated in k The battery changes speed at all times.

4. A battery explosion-proof performance test method based on big data according to claim 3, characterized in that: The method of combining the battery state equation with the battery observation equation and using the particle filtering technology to obtain the state particle set estimation response time of the battery generating thermal runaway at a preset time comprises the following steps: S231, setting heating time based on particle filtering technology k , generate initial particles and particle weights to complete the initialization parameter setting, and obtain the battery state equation k The predicted value of the state at the moment; S232, obtain the battery temperature distribution result k The internal temperature value of the battery at the moment, and the characteristic relationship between temperature and heating frequency is established to reflect the battery k The changing laws of time; S233, based on the law of change, the initial particle weights are normalized and resampled to generate new particle estimates k The posterior probability distribution of the battery producing thermal runaway at the moment is calculated, and the response time of the battery when thermal runaway occurs is estimated by the posterior probability distribution result.

5. A battery explosion-proof performance test method based on big data according to claim 4, characterized in that: The step of analyzing the response characteristics under heat source conditions based on the response time and using the response characteristics as a basis to construct an evaluation model to determine the battery's tolerance and evaluate the battery's damage degree value comprises the following steps: S241, analyzing the battery state change response relationship according to the relationship between the response time analysis and the heating frequency and temperature setting, and fitting the response relationship to describe the response characteristics using an adaptive optimal segmentation method; S242, determining the fluctuation of the battery's tolerance during the thermal runaway process within the response time according to the response characteristics, and constructing a response characteristic matrix based on the fluctuation and the response characteristics; S243, using the response characteristic matrix as an adoption condition combined with the scenario generation method to simulate the fluctuation of bearing capacity, and using the fluctuation results as the basis to construct the assessment model; S234. Evaluate the degree of damage to the battery by predicting the maximum tolerance of the battery under heat source conditions based on the evaluation model.

6. A battery explosion-proof performance test method based on big data according to claim 1, characterized in that: The method of extracting vibration characteristics based on vibration response data and combining them with material properties to establish a stress analysis model to determine the stress distribution of the secondary test object under vibration conditions includes the following steps: S441, identifying the resonant frequency of the secondary test object according to the vibration response data as a vibration feature, and collecting the elastic modulus, Poisson's ratio, and yield strength of the secondary test object as material properties; S442. Use finite element analysis software to establish a stress analysis model based on vibration characteristics and material properties to obtain the maximum stress when component displacement and cavity gap change occur; S443, verifying the stress analysis model based on the maximum stress, and setting acceleration response change conditions in the stress analysis model to obtain several groups of maximum stress distribution conditions; S444. Analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions based on the maximum stress value combined with support vector machine.

7. A battery explosion-proof performance test method based on big data according to claim 6, characterized in that: The stress distribution value of each component of the secondary test object under vibration conditions is analyzed based on the maximum stress value combined with the support vector machine, comprising the following steps: S4441. Obtaining the location of a stress concentration area based on several groups of maximum stress distribution conditions, and determining the corresponding vibration conditions within the area location; S4442, obtaining vibration conditions corresponding to the maximum stress distribution of the selected preset component, performing data standardization and dimensionality reduction processing as a training set; S4443. Substitute the training set into the support vector machine for training, and use the trained support vector machine to analyze the stress distribution values ​​of each component of the secondary test object under vibration conditions.

8. A battery explosion-proof performance test system based on big data, used to implement the battery explosion-proof performance test method based on big data as described in any one of claims 1 to 7, characterized in that: The battery explosion-proof performance test system comprises a test platform construction module (1), a damage degree judgment module (2), a comparison and rejection setting module (3) and an explosion-proof performance test module (4), wherein the test platform construction module (1), the damage degree judgment module (2), the comparison and rejection setting module (3) and the explosion-proof performance test module (4) are connected in sequence; The test platform building module (1) is used to build a test platform using structural analysis technology and test instruments, and to test and record the reaction process of the battery through the test platform based on a pre-set heat source test plan; The damage degree judgment module (2) is used to evaluate the response time of the battery to generate thermal runaway under heat source conditions by using the reaction process, and to construct an evaluation model based on the response time and energy transfer technology to determine the damage degree value of the battery; The comparison and rejection setting module (3) is used to compare the damage degree value with a preset damage value, set batteries with a damage value less than the preset value as preliminary qualified products, and set batteries with a damage value greater than the preset value as unqualified products for rejection; The explosion-proof performance test module (4) is used to perform vibration response analysis on preliminary qualified products using a virtual excitation method, and to build a stress analysis model based on the response analysis results to obtain the explosion-proof performance of the battery under vibration.

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