A battery shell low temperature deformation resistance test method and system

Abstract

The present invention relates to the field of battery shell testing technology, specifically to a battery shell low-temperature anti-deformation test method and system, including the steps of: performing low-temperature environment simulation processing on the battery shell to be tested to obtain a low-temperature quantum data set, adjusting the temperature parameters of the low-temperature environment and performing propagation simulation to obtain low-temperature temperature difference fluctuation data; generating microscopic stress distribution data based on the molecular displacement data and low-temperature temperature difference fluctuation data of the battery shell; performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, identifying stress concentration areas at different frequencies in the frequency domain data, and generating stress spectrum data; constructing a strain relationship model based on the stress spectrum data and simulating the processing, obtaining the deformation data and stress distribution data of the battery shell in a low-temperature environment and performing fusion processing to generate the test data of the battery shell. This application improves the safety assessment capability of the battery shell in a low-temperature environment and provides a more accurate testing method for the safety of the battery shell.

Description

Technical Field

[0001] The present invention relates to the technical field of battery shell testing, and in particular to a method and system for testing the low-temperature deformation resistance of a battery shell. Background Art

[0002] With the continuous development of electric vehicles (EV) and rechargeable battery technology, battery performance has become one of the key factors affecting vehicle performance and safety. Especially in low-temperature environments, the working efficiency and safety of the battery will be significantly affected. As an important component of the battery, the anti-deformation performance of the battery casing is directly related to the safety and reliability of the battery. However, existing low-temperature deformation resistance test methods for battery casings usually rely on traditional physical testing methods, such as testing the deformation of the battery casing through low-temperature environment simulation experiments. Although this type of test method can provide a preliminary evaluation of the low-temperature performance of the battery casing, it has certain limitations in analysis accuracy and data depth, and cannot fully reflect the dynamic stress distribution and microscopic deformation behavior of the battery casing in a complex low-temperature environment.

[0003] Most of the current low-temperature deformation resistance tests are based on the data collection of macroscopic stress and deformation, ignoring the microscopic physical processes inside the battery shell. For example, existing methods usually measure physical quantities such as temperature, stress and deformation through external sensors. Although these data can provide the overall performance of the battery shell in a low-temperature environment, they fail to go deep into the microstructure level of the battery shell and cannot reveal the specific impact of changes in molecular structure under different temperature gradients on the deformation behavior of the shell. This makes it difficult for existing test methods to identify stress concentration areas and potential microscopic defects, and it is also difficult to comprehensively evaluate the safety of the battery shell, especially under extreme conditions in a low-temperature environment. The battery shell may rupture or deform due to the accumulation of microscopic defects, and existing testing methods fail to provide effective early warning for this. Summary of the invention

[0004] The main purpose of the present invention is to provide a battery shell low-temperature anti-deformation test method and system, aiming to overcome the technical problem that the existing battery shell test method has insufficient ability to evaluate the microscopic deformation behavior and molecular structure changes in a low-temperature environment.

[0005] In order to achieve the above-mentioned invention problem, the present invention proposes a method for testing the low-temperature deformation resistance of a battery casing, the method comprising: Performing low temperature environment simulation processing on the battery shell to be tested to obtain a low temperature quantum data set, wherein the low temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; Adjusting the temperature parameters of the low-temperature environment, and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; Acquiring molecular displacement data on the surface and inside of the battery housing, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; Performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; Building a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery shell in a low temperature environment; The deformation data and stress distribution data are fused and processed to generate test data of the battery casing.

[0006] Furthermore, the step of performing low temperature environment simulation processing on the battery housing to be tested to obtain a low temperature quantum data set includes: Acquiring preliminary temperature distribution data of the battery housing in the low-temperature cooling device, wherein the preliminary temperature distribution data includes real-time temperature values ​​of various points on the surface and inside of the battery housing; Converting the preliminary temperature distribution data into a quantum state form to obtain temperature-based quantum state data, wherein the quantum state data includes a quantum state distribution corresponding to the real-time temperature value; Performing quantum fluctuation simulation processing on the quantum state data to obtain quantum fluctuation data, wherein the quantum fluctuation data includes the frequency, amplitude and fluctuation propagation path of the fluctuation; The quantum fluctuation data and the preliminary temperature distribution data are subjected to heat conduction simulation calculation to obtain the low-temperature quantum data set.

[0007] Furthermore, the step of adjusting the temperature parameters of the low-temperature environment and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data includes: Performing spatial discretization processing on the temperature distribution data in the low-temperature quantum data set to obtain temperature data at different positions on a plurality of spatial grid points; Establishing a heat conduction equation model under low temperature environment based on the temperature data, and generating distribution data of each of the grid points based on the heat conduction equation model; Performing quantum state propagation simulation on the quantum fluctuation data in the cryogenic quantum data set, calculating the propagation process of the quantum state, and obtaining the fluctuation data of the quantum state at different spatial positions; Combining the distribution data and the fluctuation data, simulating the local temperature difference fluctuation caused by the temperature gradient based on a thermoacoustic coupling algorithm to obtain temperature difference fluctuation data; The low-temperature temperature difference fluctuation data is matched with the material characteristic parameters of the battery shell to obtain temperature difference fluctuation distribution data corresponding to different regions, wherein the material characteristic parameters at least include the thermal expansion coefficient and specific heat capacity of the battery shell.

[0008] Furthermore, the step of obtaining the molecular displacement data on the surface and inside of the battery housing, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data, comprises: Based on the acquisition sensor, the spatiotemporal data of the surface and internal molecules of the battery shell are collected to obtain the original molecular displacement data of the battery shell; Performing multi-scale analysis on the molecular displacement raw data to obtain a molecular displacement curve, and constructing a thermal expansion model based on the displacement curve; Inputting the temperature difference fluctuation data into the thermal expansion model as a correction factor, performing thermal effect correction on the original molecular displacement data, and obtaining corrected molecular displacement data; Obtaining the displacement change rate of each molecule in the molecular displacement data, and calculating the magnitude and direction of the force borne by each molecule at different time points based on the displacement change rate to generate molecular force data; The molecular force data and the low-temperature temperature difference fluctuation data are superimposed to obtain microscopic stress distribution data.

[0009] Furthermore, the step of performing Fourier transform on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data comprises: The micro stress distribution data is converted from time domain data to frequency domain data based on a Fourier transform algorithm, wherein the frequency domain data includes stress fluctuation amplitude and phase information at different frequencies; Dividing different frequency intervals according to the frequency domain data, and extracting stress fluctuation information within a specific frequency range, wherein the specific frequency range is a main stress fluctuation frequency band of the battery casing under a low temperature environment; Performing amplitude screening on the stress fluctuation information to obtain frequency information with significant stress fluctuation, wherein the frequency information includes amplitude distribution information; Identifying stress concentration areas at different frequencies based on the amplitude distribution information, wherein the stress concentration area is an area showing relatively large amplitude fluctuations within the specific frequency range; A frequency spectrum of stress fluctuation is calculated based on the stress concentration area to generate stress spectrum data, wherein the stress spectrum data includes a relationship diagram between frequency and stress amplitude.

[0010] Furthermore, the step of constructing a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery housing in a low temperature environment includes: Establishing a stress-strain relationship data set based on the stress concentration area characteristics in the stress spectrum data, and constructing a preliminary relationship model according to the frequency response coefficient in the stress-strain relationship data set; Building a temperature-strain coupling function model based on the preliminary relationship model, and generating a temperature-strain mapping relationship under a low temperature environment based on the temperature-strain coupling function model; A time step of a preset number of digits is set, and multiple numerical iteration calculations are performed on the temperature-strain mapping relationship based on the time step to obtain the microscopic deformation state of the battery shell at different time nodes; Calculating the stress distribution and deformation distribution of the microscopic deformation state based on the thermal expansion coefficient of the material of the battery shell to obtain a low-temperature stress distribution diagram and deformation data of the battery shell; The low-temperature stress distribution diagram is fused with the deformation data to obtain the deformation data and stress distribution data of the battery casing in a low-temperature environment.

[0011] Furthermore, the step of fusing the deformation data and the stress distribution data to generate the test data of the battery casing includes: Performing multi-dimensional spatial alignment processing on the deformation data and the stress distribution data to obtain an aligned data set; Performing data weighted fusion processing on the aligned data sets to obtain comprehensive deformation-stress data; Regularizing the comprehensive deformation-stress data to obtain a standardized data set; Performing multi-scale analysis on the deformation data and stress distribution data according to the standardized data set to obtain multi-level deformation-stress response data; Performing cross-validation processing on the multi-level deformation-stress response data to obtain verified fusion data; The verified fusion data is subjected to stress and deformation trend analysis to obtain test data of the battery housing in a low temperature environment, wherein the test data at least includes a deformation and stress distribution trend diagram.

[0012] The present invention also provides a battery casing low temperature anti-deformation testing system, comprising: A simulation module, used to perform low temperature environment simulation processing on the battery housing to be tested to obtain a low temperature quantum data set, wherein the low temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; An adjustment module, used for adjusting the temperature parameters of the low-temperature environment, and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; An acquisition module, used to acquire molecular displacement data on the surface and inside of the battery housing, and generate micro stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; A transformation module, used for performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; A construction module, used to construct a strain relationship model based on the stress spectrum data, perform numerical simulation processing on the strain relationship model, and obtain deformation data and stress distribution data of the battery shell in a low temperature environment; A generation module is used to fuse the deformation data and the stress distribution data to generate test data of the battery shell.

[0013] The present invention further provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented.

[0015] Beneficial effects: The present application proposes a method and system for testing the low-temperature deformation resistance of a battery shell. By simulating the low-temperature environment of the battery shell, a low-temperature quantum data set is obtained, which includes temperature distribution data and quantum fluctuation data, thereby providing more detailed and accurate basic data for the low-temperature deformation resistance behavior of the battery shell. By adjusting the temperature parameters of the low-temperature environment and performing propagation simulation based on the simulation device, the method can obtain low-temperature temperature difference fluctuation data, and generate microscopic stress distribution data in combination with the molecular displacement data on the surface and inside of the battery shell, which can analyze the overall deformation of the battery shell, and can also analyze in detail the changes in the microstructure inside the battery shell and its influence on the deformation behavior under different temperature gradients. Further, the microscopic stress distribution data is converted into the frequency domain by Fourier transform, the stress fluctuation information is extracted, and the stress concentration area at different frequencies in the stress fluctuation information is analyzed to generate stress spectrum data, which can more accurately analyze the distribution of stress at different frequencies, and obtain the deformation data and stress distribution data of the battery shell in a low-temperature environment through numerical simulation, and generate more comprehensive test data. These data can provide battery manufacturers with a more reliable design basis and further improve the safety and reliability of the battery.

[0016] In summary, the battery shell low-temperature anti-deformation test method of the present application not only overcomes the deficiencies in analysis accuracy and data depth in the prior art, but also comprehensively improves the safety assessment capability of the battery shell under low-temperature environments through a detailed analysis of microscopic physical processes, providing a more accurate testing method for the reliability and safety of the battery shell under extreme low-temperature conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the steps of a method for testing the low temperature deformation resistance of a battery casing in one embodiment of the present invention; Figure 2 It is a schematic structural block diagram of a battery casing low temperature anti-deformation testing system according to an embodiment of the present invention; Figure 3 is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0019] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "above", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of features, integers, steps, operations, elements, modules, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and / or groups thereof. It should be understood that when an element is said to be "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any module and all combinations of one or more associated listed items.

[0020] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0021] Reference Figure 1 The embodiment of the present invention provides a method for testing the low temperature deformation resistance of a battery housing, comprising the following steps: S1: performing laser fine-tuning processing on the joint surface of the battery shell to obtain image data of the microscopic lattice of the joint surface after the laser fine-tuning processing; In step S1, the battery shell to be tested is subjected to low temperature environment simulation processing to obtain a low temperature quantum data set. The main goal of the low temperature environment simulation processing is to evaluate the changes in the physical properties of the battery shell at low temperatures, especially the changes in properties such as stress and deformation, by simulating the actual conditions of the low temperature environment. In this process, the simulation device is used to create a realistic low temperature environment, simulating the possible impact of real low temperature conditions on the battery shell, which can be carried out through specific environmental simulation equipment such as a low temperature box, a low temperature climate test chamber, etc. In a low temperature environment, the material of the battery shell will experience some special physical phenomena, such as thermal expansion and contraction, molecular motion changes, etc. These changes will affect the strength and deformation resistance of the battery shell. The temperature changes of the battery shell in a low temperature simulation environment are recorded through precise temperature sensors and monitoring equipment. The collection of these data can be achieved through temperature sensors at multiple locations, and quantum fluctuation data captures the changes in the battery shell at the quantum level in a low temperature environment. Quantum fluctuation data reflects the microscopic characteristics of the atomic and molecular motion of matter under extremely low temperature conditions. In a low temperature environment, the vibration of atoms and molecules will change significantly, and high-precision sensor equipment and advanced quantum computing technology are used to analyze the quantum fluctuations of materials. For example, quantum computers can simulate the interactions between molecules at low temperatures and predict the movement patterns of different molecules in low-temperature environments.

[0022] In another embodiment, a quantum heat conversion device is designed to accurately distribute the temperature on the surface of the shell material through the superposition and entanglement effect of quantum bits through the principle of heat control and transfer at the quantum level. The device does not use traditional liquid nitrogen or refrigeration liquid, but uses the phase change of quantum state to adjust the temperature, so that the low-temperature environment achieves unprecedented stability. Using quantum fluctuation field regulation technology, a quantum field regulator is used to establish a highly dynamic low-temperature field around the battery shell. This field can not only adjust the temperature in real time, but also cause extremely subtle temperature differences, thereby generating tiny stress concentration points on the shell surface. A quantum particle beam is used to form a contactless low-temperature cooling environment around the battery shell, avoiding direct damage to the battery shell surface that may be caused by traditional cooling methods. Through the dynamic scattering of quantum particles, the duration and change rate of the low-temperature area are accurately controlled to simulate different extremely low-temperature environmental effects.

[0023] S2: adjusting the temperature parameters of the low-temperature environment, performing propagation simulation on the low-temperature quantum data set based on a simulation device, and obtaining low-temperature temperature difference fluctuation data; In step S2, by adjusting the temperature of the low-temperature environment and using a simulation device to perform dynamic propagation simulation on the acquired low-temperature quantum data set, the temperature difference fluctuation of the battery housing in the low-temperature environment is analyzed. This can be achieved by adjusting the temperature in the low-temperature simulation device in step S1, and propagation simulation is performed based on the low-temperature quantum data set. Propagation simulation is a method of using numerical simulation to study the effect of temperature changes on the behavior of materials inside the battery housing through the collected low-temperature quantum data set. The simulation device used in this stage is a computer simulation software or a high-precision simulation platform. These tools can accurately predict the behavior of the battery housing in a low-temperature environment based on physical principles. Through these simulation methods, low-temperature temperature difference fluctuation data can be obtained to describe the temperature difference changes and fluctuations of the battery housing in a low-temperature environment. In actual operation, the simulation device can track the process of temperature difference propagation inside the battery housing through algorithm calculation, simulate the fluctuation of temperature difference, and obtain low-temperature temperature difference fluctuation data.

[0024] S3: Acquire molecular displacement data on the surface and inside of the battery housing, and generate micro stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; In step S3, under low temperature environment, the molecular displacement of the material is usually caused by the thermal expansion or contraction effect caused by temperature change. In order to obtain these molecular displacement data, molecular dynamics simulation or advanced surface scanning technology, such as scanning electron microscope (SEM) or atomic force microscope (AFM), etc., can be used to perform high-precision imaging and measurement in low temperature environment, and capture the molecular displacement of the surface of the battery shell or even the internal materials. For example, the atomic force microscope can accurately measure the position change of each molecule or atom at low temperature by scanning the surface of the battery shell, thereby obtaining high-resolution data of molecular displacement. After obtaining the molecular displacement data, it is combined with the low-temperature temperature difference fluctuation data, and the relationship between the two is comprehensively analyzed to generate microscopic stress distribution data, which can accurately simulate the stress distribution of different areas inside and outside the battery shell on a microscopic scale.

[0025] S4: performing Fourier transform on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; In step S4, the complex time or space variation signal is converted into a set of analyzable frequency components by Fourier transform. For the battery shell, the microscopic stress distribution data is expressed as a series of stress values ​​that vary with position or time. The Fourier transform can convert these time-varying or space-varying stress data into frequency domain data, extract stress fluctuation information in the frequency domain data, and mainly identify stress concentration areas at different frequencies. Stress fluctuations are usually manifested as different frequency peaks in the frequency domain, which can indicate which areas have more intense or concentrated stress distributions under low temperature environments. By analyzing these frequency peaks and their corresponding stress values, it can be determined whether certain areas of the battery shell are under greater stress at certain specific frequencies. In this process, the frequency domain data can be processed and analyzed by analysis tools. The frequency domain data after Fourier transformation will be presented in the form of a spectrum diagram, and the characteristics and concentrated areas of stress fluctuations can be identified by observing the peak position and amplitude in the spectrum diagram. In actual operation, software tools such as the Fourier transform library in MATLAB or Python can be used to implement this process, and key information in the spectrum data can be automatically extracted through programming. After identifying the stress concentration areas at different frequencies, stress spectrum data is generated, which can show the stress fluctuations at different frequencies.

[0026] S5: constructing a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery housing under a low temperature environment; In step S5, the strain relationship model describes the mathematical relationship between stress and strain through the stress-strain theory in physics, especially the linear or nonlinear elastic theory. Based on the frequency components in the stress spectrum data, a stress-strain model for the battery shell under low temperature environment can be established. For example, when the stress spectrum data shows obvious stress concentration at certain frequencies, the data of these frequencies can be used to derive the strain response, reflecting the deformation behavior of the battery shell under these frequencies. Based on the constructed strain relationship model, numerical simulation processing is performed, and the unknown parameters in the model are solved by mathematical methods to predict the behavior of the system. In this step, the main goal of the numerical simulation is to solve the deformation and stress distribution of the battery shell under low temperature conditions. This process can often use methods such as finite element analysis (FEA) to discretize the geometric model of the battery shell into multiple small units, and each unit can independently calculate its stress and strain response under low temperature environment. In this way, the simulation can provide the deformation mode of the battery shell at low temperature, the stress distribution in each region and its change trend. Through numerical simulation, the degree of deformation of these regions can be calculated, and the type of deformation that may occur in the battery shell at low temperature can be predicted, such as local plastic deformation, cracks or fractures. Furthermore, numerical simulation can be used to obtain stress distribution data of the battery case in a low-temperature environment, showing the stress state of each part of the battery case under low-temperature conditions, and reflecting which areas are under greater stress and which areas are relatively less stressed. By comparing these data with actual test results, the accuracy of the simulation results can be verified, and the design of the battery case can be optimized based on the simulation results.

[0027] S6: fusing the deformation data and stress distribution data to generate test data of the battery casing.

[0028] In step S6, the purpose of fusing the deformation data and stress distribution data is to obtain a more comprehensive and accurate test data through the interaction between the two, describing the actual performance of the battery shell in a low temperature environment. Although the deformation data and stress distribution data reflect different aspects of the battery shell, they are closely related. Deformation is closely related to stress, and areas with greater stress often cause greater deformation. In this process, the deformation data and stress distribution data can be fused through a mathematical model (such as a finite element analysis model), and the deformation of each part is predicted according to the intensity distribution of the stress data, thereby generating a complete set of test data covering the comprehensive performance of the battery shell at low temperatures. The specific fusion process can be achieved through numerical analysis methods. For example, the weighted average method can be used to combine the deformation data and the stress distribution data. Through these methods, a comprehensive data set reflecting the low-temperature deformation resistance of the battery shell can be obtained. These test data will include the stress and deformation of the battery shell at different temperatures, which can be used to evaluate the deformation resistance of the battery shell in a low temperature environment. For example, if the test data shows that the stress in certain areas is too large and the deformation is obvious, it may be necessary to re-evaluate the material selection or structural design of the part to avoid damage to the battery shell in actual use. In this way, the fused test data provides reliable quantitative results for the low-temperature deformation resistance test of the battery shell, and also provides decision-making support for subsequent engineering optimization.

[0029] In one embodiment, the step of performing low temperature environment simulation processing on the battery housing to be tested to obtain a low temperature quantum data set includes: Acquiring preliminary temperature distribution data of the battery housing in the low-temperature cooling device, wherein the preliminary temperature distribution data includes real-time temperature values ​​of various points on the surface and inside of the battery housing; Converting the preliminary temperature distribution data into a quantum state form to obtain temperature-based quantum state data, wherein the quantum state data includes a quantum state distribution corresponding to the real-time temperature value; Performing quantum fluctuation simulation processing on the quantum state data to obtain quantum fluctuation data, wherein the quantum fluctuation data includes the frequency, amplitude and fluctuation propagation path of the fluctuation; The quantum fluctuation data and the preliminary temperature distribution data are subjected to heat conduction simulation calculation to obtain the low-temperature quantum data set.

[0030] In the above embodiment, in order to simulate the low temperature environment, the battery housing is placed in a low temperature cooling device, in which the surface and different positions inside the battery housing are adjusted to reach the set low temperature state. By installing temperature sensors at different positions, the temperature changes at various points of the battery housing can be monitored in real time, and then the real-time temperature values ​​of the surface and inside of the battery housing are collected, which can reflect the temperature gradient and temperature distribution of each part under the low temperature state. Combining temperature data with the theory of quantum mechanics, quantum state data is a representation of temperature distribution, and the microscopic physical state of the battery housing in a low temperature environment is described by the model of quantum mechanics. Specifically, the difference in temperature is manifested as different energy states at the molecular or atomic level. The lower the temperature, the lower the vibration frequency of the molecule or atom, so the temperature data can be mapped to the corresponding quantum state. In this process, the obtained quantum state data not only includes the real-time temperature values ​​of the surface and internal points of the battery housing, but also can further analyze the quantum state distribution corresponding to each temperature value, that is, the quantum state describing how the microscopic particles at these different temperatures are arranged or vibrated. The behavior of the battery housing in a low temperature environment is understood by simulating the characteristics of quantum fluctuations. Quantum fluctuations describe the fluctuation behavior of quantum particles (such as electrons, atoms, etc.) in space and time. At low temperatures, the molecules of matter inside the battery shell may exhibit strong wave characteristics, especially when affected by external temperature changes. Through quantum wave simulation processing, detailed data about these fluctuations can be obtained, and then the quantum wave data and preliminary temperature distribution data can be simulated for heat conduction. The solution to the heat conduction problem can be obtained through algorithms such as the Green's Function Method, which can reveal how heat propagates inside the battery shell, how it affects the temperature distribution of various parts of the battery shell through different paths, and how temperature changes interact with quantum fluctuations. By comprehensively calculating the results, a low-temperature quantum data set can be obtained. This data set contains basic information about the temperature distribution, combined with the complex interaction of quantum fluctuations and heat conduction, and can more comprehensively and accurately reflect the behavior of the battery shell in a low-temperature environment.

[0031] In one embodiment, the step of adjusting the temperature parameters of the low-temperature environment and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data includes: Performing spatial discretization processing on the temperature distribution data in the low-temperature quantum data set to obtain temperature data at different positions on a plurality of spatial grid points; Establishing a heat conduction equation model under low temperature environment based on the temperature data, and generating distribution data of each of the grid points based on the heat conduction equation model; Performing quantum state propagation simulation on the quantum fluctuation data in the cryogenic quantum data set, calculating the propagation process of the quantum state, and obtaining the fluctuation data of the quantum state at different spatial positions; Combining the distribution data and the fluctuation data, simulating the local temperature difference fluctuation caused by the temperature gradient based on a thermoacoustic coupling algorithm to obtain temperature difference fluctuation data; The low-temperature temperature difference fluctuation data is matched with the material characteristic parameters of the battery shell to obtain temperature difference fluctuation distribution data corresponding to different regions, wherein the material characteristic parameters at least include the thermal expansion coefficient and specific heat capacity of the battery shell.

[0032] In the above embodiment, the temperature distribution data in the low-temperature quantum data set is spatially discretized, and the continuous temperature data is converted into a discrete form, so that the temperature value of each spatial position can be analyzed separately. By setting multiple spatial grid points at different positions of the battery shell, the temperature data can be collected and calculated at these points. Each grid point represents the temperature value of a specific position of the battery shell, and these temperature values ​​reflect the actual temperature distribution of the battery shell in a low-temperature cooling environment. The discretization process converts the temperature field in space into limited numerical data. Based on these discretized temperature data, a heat conduction equation model is established in a low-temperature environment, and the heat transfer process of the battery shell in a low-temperature environment is simulated by the application of the heat conduction equation. In this model, the temperature data is used as a known condition, combined with the physical properties of the battery shell, to calculate the heat distribution of each grid point, simulate the heat transfer of the battery shell in a low-temperature environment, and obtain the temperature change data at each grid point. After the temperature distribution model is established, the quantum fluctuation data in the low-temperature quantum data set is subjected to quantum state propagation simulation, and the quantum fluctuation phenomenon in a low-temperature environment is simulated by the principles of quantum mechanics. Through the quantum state propagation simulation, the fluctuation data of the quantum state at different spatial positions can be obtained, that is, the specific data describing how particles propagate and oscillate in a low-temperature environment. After combining the temperature distribution data, heat conduction model and quantum fluctuation data, the algorithm based on thermoacoustic coupling simulates the local temperature difference fluctuation caused by the temperature gradient. This is because in practical applications, the temperature gradient in different areas inside the battery shell will cause local temperature difference fluctuations, thereby affecting the deformation resistance of the battery shell. The thermoacoustic coupling algorithm combines the temperature gradient with the acoustic fluctuation to simulate how the temperature change causes local temperature difference fluctuations in the material and obtain the temperature difference fluctuation data. The obtained low-temperature temperature difference fluctuation data needs to be matched with the material characteristic parameters of the battery shell to obtain more accurate temperature difference fluctuation distribution data. These material characteristic parameters include thermal expansion coefficient and specific heat capacity, which determine the response of the material to temperature changes. For example, the thermal expansion coefficient determines the degree of expansion or contraction of the material when the temperature changes, while the specific heat capacity reflects the degree of temperature change of the material when absorbing heat. By combining these material properties with the temperature difference fluctuation data, the deformation and performance of the battery shell under different temperature conditions can be predicted, thereby providing data support for the design optimization and performance evaluation of the battery shell.

[0033] In another embodiment, the calculation expression of the above embodiment is: ,in, It represents the displacement change (i.e. deformation) of the battery shell at time t and spatial position r. α is the thermal expansion coefficient, with the unit of 1 / K, which represents the degree of expansion or contraction of the material when the temperature changes. It is the Laplace operator of temperature distribution, represents the diffusion process of heat, reflects the spatial gradient of temperature, and is related to the thermal conductivity characteristics of the material. is the temperature field, which represents the temperature at time t and spatial position r. β is the influence coefficient of quantum state propagation, with the unit of , used to quantify the effects of quantum fluctuations on materials, related to temperature-induced changes in the microstructure. The wave data representing the propagation of quantum states describes the impact of quantum fluctuations in low-temperature environments and reflects the state changes of particles at different positions. The Laplace operator represents the second derivative of space and is used to describe the expansion or change of the temperature field. t is the time variable, which represents the heat conduction process and quantum wave propagation that changes with time.

[0034] In one embodiment, the step of obtaining the molecular displacement data on the surface and inside of the battery housing, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data, comprises: Based on the acquisition sensor, the spatiotemporal data of the surface and internal molecules of the battery shell are collected to obtain the original molecular displacement data of the battery shell; Performing multi-scale analysis on the molecular displacement raw data to obtain a molecular displacement curve, and constructing a thermal expansion model based on the displacement curve; Inputting the temperature difference fluctuation data into the thermal expansion model as a correction factor, performing thermal effect correction on the original molecular displacement data, and obtaining corrected molecular displacement data; Obtaining the displacement change rate of each molecule in the molecular displacement data, and calculating the magnitude and direction of the force borne by each molecule at different time points based on the displacement change rate to generate molecular force data; The molecular force data and the low-temperature temperature difference fluctuation data are superimposed to obtain microscopic stress distribution data.

[0035] In the above embodiment, the surface and interior of the battery shell are collected by collecting spatiotemporal data of the acquisition sensor, so as to obtain the original data of the molecular displacement of the battery shell. The displacement of the molecules at different time points can be monitored in real time by the sensor to obtain spatiotemporal data. These data reflect the subtle displacement of the molecules inside the battery shell under the action of low temperature, have high spatial resolution, and can cover all levels of the battery shell. These data are processed and analyzed at different scales to obtain a clearer molecular displacement curve. Multi-scale analysis is to obtain a molecular displacement curve at the global and local levels by different effects of physical phenomena at different scales on displacement. Through multi-scale analysis, these different deformation mechanisms can be integrated into a unified analysis framework, and then an effective thermal expansion model can be constructed. By combining temperature data with displacement curves, a thermal expansion model can be formed to describe how the molecular displacement of the battery shell changes under different temperature changes. The model mainly relies on the thermal expansion coefficient and describes the relationship between the temperature change and volume or deformation of the material. Introducing low temperature temperature difference fluctuation data into the model, using it as a correction factor to correct the molecular displacement data, the thermal expansion model can be further corrected to obtain more accurate molecular displacement data. Obtain the displacement change rate of each molecule at different times. This process needs to be based on the original molecular displacement data, and the displacement rate of each molecule is calculated. The displacement change rate determines the magnitude and direction of the force exerted on the molecule at different time points. Using the calculated molecular force data, it can be superimposed with the low-temperature temperature difference fluctuation data. The purpose of this process is to obtain more comprehensive and accurate micro-stress distribution data by combining the effects of both. By superimposing the molecular force data and the temperature difference fluctuation data, the micro-stress state of the battery casing under low temperature conditions can be better reflected.

[0036] In one embodiment, the steps of performing Fourier transform on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data include: The micro stress distribution data is converted from time domain data to frequency domain data based on a Fourier transform algorithm, wherein the frequency domain data includes stress fluctuation amplitude and phase information at different frequencies; Dividing different frequency intervals according to the frequency domain data, and extracting stress fluctuation information within a specific frequency range, wherein the specific frequency range is a main stress fluctuation frequency band of the battery casing under a low temperature environment; Performing amplitude screening on the stress fluctuation information to obtain frequency information with significant stress fluctuation, wherein the frequency information includes amplitude distribution information; Identifying stress concentration areas at different frequencies based on the amplitude distribution information, wherein the stress concentration area is an area showing relatively large amplitude fluctuations within the specific frequency range; A frequency spectrum of stress fluctuation is calculated based on the stress concentration area to generate stress spectrum data, wherein the stress spectrum data includes a relationship diagram between frequency and stress amplitude.

[0037] In the above embodiment, the Fourier transform algorithm converts the original time domain data into frequency domain data. In the time domain, the microscopic stress distribution data that changes with time is obtained. These data contain the stress values ​​at different time points. The time domain data is converted into frequency domain data through Fourier transform. The basic principle of Fourier transform is to decompose a complex time domain signal into a superposition of a set of sinusoidal signals, each of which corresponds to a specific frequency component. After Fourier transform, the obtained frequency domain data not only contains the stress amplitude and phase information of different frequency components, but also these information can help us deeply understand the response behavior of the battery shell at different frequencies. According to the frequency domain data, different frequency intervals are divided, and stress fluctuation information within a specific frequency range is extracted. This frequency band is caused by the stress response of the material under low temperature conditions, or the battery shell is affected by the change of external environmental temperature. According to the experimental data, these frequency ranges can be determined by frequency analysis methods, and the stress fluctuation information within this specific frequency range can be concentrated, thereby excluding other irrelevant frequency components. The extracted stress fluctuation information will be filtered by amplitude to filter out those frequency information with significant stress fluctuations from the frequency domain data. By screening out frequency components with larger amplitudes, we can focus on those frequency bands that have a greater impact on the deformation resistance of the battery shell. After obtaining the amplitude distribution information, the stress concentration areas at different frequencies are identified based on this information. The stress concentration area refers to the area within a certain frequency range where the stress amplitude of the battery shell fluctuates greatly, which may lead to stress concentration in local areas. By conducting a detailed analysis of the stress fluctuations at different frequencies, we can determine which areas exhibit larger stress fluctuations at certain specific frequencies. Based on the identified stress concentration areas, the frequency spectrum of the stress fluctuations is calculated, and the stress spectrum data is further generated. The stress spectrum data shows the relationship between different frequencies and stress amplitudes.

[0038] In one embodiment, the step of constructing a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery housing in a low temperature environment includes: Establishing a stress-strain relationship data set based on the stress concentration area characteristics in the stress spectrum data, and constructing a preliminary relationship model according to the frequency response coefficient in the stress-strain relationship data set; Building a temperature-strain coupling function model based on the preliminary relationship model, and generating a temperature-strain mapping relationship under a low temperature environment based on the temperature-strain coupling function model; A time step of a preset number of digits is set, and multiple numerical iteration calculations are performed on the temperature-strain mapping relationship based on the time step to obtain the microscopic deformation state of the battery shell at different time nodes; Calculating the stress distribution and deformation distribution of the microscopic deformation state based on the thermal expansion coefficient of the material of the battery shell to obtain a low-temperature stress distribution diagram and deformation data of the battery shell; The low-temperature stress distribution diagram is fused with the deformation data to obtain the deformation data and stress distribution data of the battery casing in a low-temperature environment.

[0039] In the above embodiment, according to the stress concentration area characteristics in the stress spectrum data, constructing a stress-strain relationship data set is the starting point of the process. In the spectrum data, the stress fluctuation of the battery shell at different frequencies can be obtained. In particular, at certain frequencies, the stress fluctuation of the shell is more significant, showing stress concentration. On this basis, a stress-strain relationship data set is established to describe the relationship between stress and strain. The frequency response coefficients in the stress-strain relationship data set provide the response characteristics of the material at different frequencies. These coefficients reflect the degree of strain of the battery shell when subjected to stress, so they can be used to construct a preliminary strain relationship model. A coupling function model of temperature and strain is constructed based on the preliminary relationship model. The deformation of the battery shell is not only affected by stress, but also closely related to temperature. Especially in low temperature environments, the physical properties of the material (such as elastic modulus and thermal expansion coefficient) will change. Through the coupling function model, the relationship between temperature change and strain can be modeled to generate a temperature-strain mapping relationship, which reveals the influence of temperature change on the strain behavior of each area of ​​the battery shell. Based on the temperature-strain mapping relationship, a preset time step is set for numerical simulation. Numerical iteration is to simulate the deformation process of the battery shell over time in a low temperature environment. In this step, the purpose of setting the time step is to discretize the time so that calculations can be performed at each time node. Through multiple iterations, the microscopic deformation state of the battery shell at different time nodes can be gradually derived. This process reflects how temperature changes gradually affect the structural morphology of the battery shell at different times. Combined with the material thermal expansion coefficient of the battery shell, the stress distribution and deformation distribution under the microscopic deformation state can be calculated, and the stress distribution diagram and deformation data of the battery shell at low temperature can be obtained. The analysis results of stress and deformation are integrated into a unified model to form a comprehensive performance evaluation result. Through this step, the behavior of the battery shell in a low temperature environment can be more comprehensively understood, and potential weak areas or stress concentration areas can be identified.

[0040] In one embodiment, the step of fusing the deformation data and the stress distribution data to generate the test data of the battery housing includes: Performing multi-dimensional spatial alignment processing on the deformation data and the stress distribution data to obtain an aligned data set; Performing data weighted fusion processing on the aligned data sets to obtain comprehensive deformation-stress data; Regularizing the comprehensive deformation-stress data to obtain a standardized data set; Performing multi-scale analysis on the deformation data and stress distribution data according to the standardized data set to obtain multi-level deformation-stress response data; Performing cross-validation processing on the multi-level deformation-stress response data to obtain verified fusion data; The verified fusion data is subjected to stress and deformation trend analysis to obtain test data of the battery housing in a low temperature environment, wherein the test data at least includes a deformation and stress distribution trend diagram.

[0041] In the above embodiment, these two types of data are mapped into a unified spatial coordinate system, and these data can be aligned by a spatial alignment algorithm (such as interpolation, coordinate transformation, etc.) to obtain an aligned data set. The aligned data set enters the weighted fusion process. At this time, the fusion of deformation data and stress distribution data is performed in a weighted manner. The purpose of the weighted fusion process of data is to integrate the contribution of various types of data to obtain a comprehensive deformation-stress data set. The selection of weighting coefficients can be based on the importance assessment of experimental data or the prediction of materials science and mechanical models to ensure that the weighted fused data can reflect the true physical state of the battery shell to the greatest extent. Converting data from different sources or scales into a unified standardized format, regularization processing can be achieved by normalizing or standardizing the data, the purpose is to eliminate the differences between different data dimensions, so that they are compared under the same standard. Multi-scale analysis is a method for in-depth analysis of data at multiple scales (time scale, spatial scale, mechanical scale, etc.). For the deformation and stress distribution of the battery shell, by integrating these different levels of factors, the deformation and stress response of the battery shell at each scale are obtained. The multi-level deformation-stress response data is cross-validated. By dividing the data set into a training set and a validation set, the data training and validation are repeated to evaluate whether the obtained deformation-stress data can truly reflect the performance of the battery shell. For example, the experimental data can be divided into multiple subsets, the model can be trained on one subset, and then tested with other subsets. By continuously verifying the effectiveness of the model, the final fusion data can be ensured to have high accuracy. Using the data after weighted fusion and cross-validation processing, the performance of the battery shell in a low temperature environment is analyzed, especially the trend changes of stress and deformation. Through trend analysis, the stress distribution and deformation mode of the battery shell at different temperatures can be obtained and displayed in a graphical way, such as a stress distribution trend graph and a deformation trend graph. These trend graphs can clearly show the deformation and stress changes of the battery shell in a low temperature environment, helping researchers predict the performance of the battery shell in practical applications and optimize its design.

[0042] Reference Figure 2 , a battery shell low temperature anti-deformation test system, comprising: A simulation module 100 is used to perform a low temperature environment simulation process on the battery housing to be tested to obtain a low temperature quantum data set, wherein the low temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; An adjustment module 200 is used to adjust the temperature parameters of the low-temperature environment, and to perform propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; An acquisition module 300 is used to acquire molecular displacement data on the surface and inside of the battery housing, and generate micro stress distribution data based on the molecular displacement data and the low temperature temperature difference fluctuation data; A transformation module 400 is used to perform Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extract stress fluctuation information in the frequency domain data, identify stress concentration areas at different frequencies in the stress fluctuation information, and generate stress spectrum data; A construction module 500 is used to construct a strain relationship model based on the stress spectrum data, and perform numerical simulation processing on the strain relationship model to obtain deformation data and stress distribution data of the battery housing in a low temperature environment; The generation module 600 is used to fuse the deformation data and the stress distribution data to generate the test data of the battery casing.

[0043] In this embodiment, the battery shell to be tested is subjected to a low-temperature environment simulation process to obtain a low-temperature quantum data set, and then the temperature parameters of the low-temperature environment are adjusted, and the low-temperature quantum data set is propagated and simulated by a simulation device to obtain low-temperature temperature difference fluctuation data, obtain the molecular displacement data on the surface and inside of the battery shell, and combine these data with the low-temperature temperature difference fluctuation data to generate microscopic stress distribution data. The microscopic stress distribution data is Fourier transformed to obtain frequency domain data, and the stress fluctuation information therein is extracted. A strain relationship model is constructed based on the stress spectrum data, and numerical simulation is performed on it to obtain the deformation data and stress distribution data of the battery shell in a low-temperature environment. The deformation data and stress distribution data are fused to generate the final test data of the battery shell. This process combines the results of all the previous steps, including temperature, stress, deformation and other data, and obtains comprehensive test data of the battery shell in a low-temperature environment through weighted fusion, standardization and other methods. For example, trend graphs and spectrum graphs of deformation and stress distribution can be obtained to help engineers evaluate the performance and reliability of the battery shell at low temperatures. Through such a test method, the anti-deformation ability of the battery shell can be effectively predicted to ensure its safety in actual use.

[0044] Reference Figure 3 In an embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as a battery shell low temperature deformation resistance test method database. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery shell low temperature deformation resistance test method is implemented.

[0045] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, a method for testing the low-temperature deformation resistance of a battery shell is implemented, comprising the following steps: performing low-temperature environment simulation processing on a battery shell to be tested to obtain a low-temperature quantum data set, wherein the low-temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; adjusting the temperature parameters of the low-temperature environment, and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; obtaining molecular displacement data on the surface and inside of the battery shell, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; constructing a strain relationship model based on the stress spectrum data, and performing numerical simulation processing on the strain relationship model to obtain deformation data and stress distribution data of the battery shell in a low-temperature environment; and fusing the deformation data and stress distribution data to generate test data of the battery shell.

[0046] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0047] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for testing the low temperature deformation resistance of a battery casing, characterized in that: include: Performing low temperature environment simulation processing on the battery housing to be tested to obtain a low temperature quantum data set, wherein the low temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; Adjusting the temperature parameters of the low-temperature environment, and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; Acquiring molecular displacement data on the surface and inside of the battery housing, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; Performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; Building a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery shell in a low temperature environment; The deformation data and stress distribution data are fused and processed to generate test data of the battery casing.

2. The battery casing low temperature deformation resistance test method according to claim 1, characterized in that: The step of performing low temperature environment simulation processing on the battery casing to be tested to obtain a low temperature quantum data set includes: Acquiring preliminary temperature distribution data of the battery housing in the low-temperature cooling device, wherein the preliminary temperature distribution data includes real-time temperature values ​​of various points on the surface and inside of the battery housing; Converting the preliminary temperature distribution data into a quantum state form to obtain temperature-based quantum state data, wherein the quantum state data includes a quantum state distribution corresponding to the real-time temperature value; Performing quantum fluctuation simulation processing on the quantum state data to obtain quantum fluctuation data, wherein the quantum fluctuation data includes the frequency, amplitude and fluctuation propagation path of the fluctuation; The quantum fluctuation data and the preliminary temperature distribution data are subjected to heat conduction simulation calculation to obtain the low-temperature quantum data set.

3. The battery casing low temperature deformation resistance test method according to claim 1, characterized in that: The step of adjusting the temperature parameters of the low-temperature environment and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data includes: Performing spatial discretization processing on the temperature distribution data in the low-temperature quantum data set to obtain temperature data at different positions on a plurality of spatial grid points; Establishing a heat conduction equation model under low temperature environment based on the temperature data, and generating distribution data of each of the grid points based on the heat conduction equation model; Performing quantum state propagation simulation on the quantum fluctuation data in the cryogenic quantum data set, calculating the propagation process of the quantum state, and obtaining the fluctuation data of the quantum state at different spatial positions; Combining the distribution data and the fluctuation data, simulating the local temperature difference fluctuation caused by the temperature gradient based on a thermoacoustic coupling algorithm to obtain temperature difference fluctuation data; The low-temperature temperature difference fluctuation data is matched with the material characteristic parameters of the battery shell to obtain temperature difference fluctuation distribution data corresponding to different regions, wherein the material characteristic parameters at least include the thermal expansion coefficient and specific heat capacity of the battery shell.

4. The battery casing low temperature deformation resistance test method according to claim 1, characterized in that: The step of obtaining the molecular displacement data on the surface and inside of the battery housing, and generating microscopic stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data, comprises: Based on the acquisition sensor, the spatiotemporal data of the surface and internal molecules of the battery shell are collected to obtain the original molecular displacement data of the battery shell; Performing multi-scale analysis on the molecular displacement raw data to obtain a molecular displacement curve, and constructing a thermal expansion model based on the displacement curve; Inputting the temperature difference fluctuation data into the thermal expansion model as a correction factor, performing thermal effect correction on the original molecular displacement data, and obtaining corrected molecular displacement data; Obtaining the displacement change rate of each molecule in the molecular displacement data, and calculating the magnitude and direction of the force borne by each molecule at different time points based on the displacement change rate to generate molecular force data; The molecular force data and the low-temperature temperature difference fluctuation data are superimposed to obtain microscopic stress distribution data.

5. The battery casing low temperature deformation resistance test method according to claim 1, characterized in that: The step of performing Fourier transform on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data comprises: The micro stress distribution data is converted from time domain data to frequency domain data based on a Fourier transform algorithm, wherein the frequency domain data includes stress fluctuation amplitude and phase information at different frequencies; Dividing different frequency intervals according to the frequency domain data, and extracting stress fluctuation information within a specific frequency range, wherein the specific frequency range is a main stress fluctuation frequency band of the battery casing under a low temperature environment; Performing amplitude screening on the stress fluctuation information to obtain frequency information with significant stress fluctuation, wherein the frequency information includes amplitude distribution information; Identifying stress concentration areas at different frequencies based on the amplitude distribution information, wherein the stress concentration area is an area showing relatively large amplitude fluctuations within the specific frequency range; A frequency spectrum of stress fluctuation is calculated based on the stress concentration area to generate stress spectrum data, wherein the stress spectrum data includes a relationship diagram between frequency and stress amplitude.

6. The battery case low temperature deformation resistance test method according to claim 1, characterized in that: The step of constructing a strain relationship model based on the stress spectrum data, performing numerical simulation processing on the strain relationship model, and obtaining deformation data and stress distribution data of the battery housing in a low temperature environment includes: Establishing a stress-strain relationship data set based on the stress concentration area characteristics in the stress spectrum data, and constructing a preliminary relationship model according to the frequency response coefficient in the stress-strain relationship data set; Building a temperature-strain coupling function model based on the preliminary relationship model, and generating a temperature-strain mapping relationship under a low temperature environment based on the temperature-strain coupling function model; A time step of a preset number of digits is set, and multiple numerical iteration calculations are performed on the temperature-strain mapping relationship based on the time step to obtain the microscopic deformation state of the battery shell at different time nodes; Calculating the stress distribution and deformation distribution of the microscopic deformation state based on the thermal expansion coefficient of the material of the battery shell to obtain a low-temperature stress distribution diagram and deformation data of the battery shell; The low-temperature stress distribution diagram is fused with the deformation data to obtain the deformation data and stress distribution data of the battery casing in a low-temperature environment.

7. The battery casing low temperature deformation resistance test method according to claim 1, characterized in that: The step of fusing the deformation data and the stress distribution data to generate the test data of the battery housing includes: Performing multi-dimensional spatial alignment processing on the deformation data and the stress distribution data to obtain an aligned data set; Performing data weighted fusion processing on the aligned data sets to obtain comprehensive deformation-stress data; Regularizing the comprehensive deformation-stress data to obtain a standardized data set; Performing multi-scale analysis on the deformation data and stress distribution data according to the standardized data set to obtain multi-level deformation-stress response data; Performing cross-validation processing on the multi-level deformation-stress response data to obtain verified fusion data; The verified fusion data is subjected to stress and deformation trend analysis to obtain test data of the battery housing in a low temperature environment, wherein the test data at least includes a deformation and stress distribution trend diagram.

8. A battery shell low temperature deformation resistance test system, characterized in that: include: A simulation module, used to perform low temperature environment simulation processing on the battery housing to be tested to obtain a low temperature quantum data set, wherein the low temperature quantum data set at least includes temperature distribution data and quantum fluctuation data; An adjustment module, used for adjusting the temperature parameters of the low-temperature environment, and performing propagation simulation on the low-temperature quantum data set based on a simulation device to obtain low-temperature temperature difference fluctuation data; An acquisition module, used to acquire molecular displacement data on the surface and inside of the battery housing, and generate micro stress distribution data based on the molecular displacement data and the low-temperature temperature difference fluctuation data; A transformation module, used for performing Fourier transformation on the microscopic stress distribution data to obtain frequency domain data, extracting stress fluctuation information in the frequency domain data, identifying stress concentration areas at different frequencies in the stress fluctuation information, and generating stress spectrum data; A construction module, used to construct a strain relationship model based on the stress spectrum data, perform numerical simulation processing on the strain relationship model, and obtain deformation data and stress distribution data of the battery shell in a low temperature environment; A generation module is used to fuse the deformation data and the stress distribution data to generate test data of the battery shell.

9. A computer device, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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