A battery pack structure strength simulation test method

Abstract

The application discloses a battery pack structure strength simulation test method, and relates to the technical field of data analysis, and comprises the following steps: obtaining the multi-layer structure parameters of a test battery pack, establishing a multi-scale geometric simulation model of the battery pack, and generating multi-scale structure simulation parameters of the test battery pack; based on the multi-scale structure simulation parameters of the test battery pack, performing cross-scale constitutive correlation analysis on the test battery pack, and evaluating the equivalent material parameter vector of the test battery pack; based on the equivalent material parameter vector of the test battery pack, performing time-frequency domain load verification, establishing a time domain-frequency domain load coupling equation, and generating the composite load distribution of the test battery pack; according to the composite load distribution of the test battery pack, establishing a test battery pack fatigue progressive failure fitting model, and generating the strength damage trend state of the test battery pack. The application has the advantages that the scale fragmentation and load simplification problems in the prior art are solved, and the simulation test precision of the battery pack structure strength is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data analysis, in particular to a battery pack structure strength simulation test method. BACKGROUND

[0002] The existing battery pack structure strength simulation mainly adopts a single scale modeling method, which cannot accurately represent the cross-scale mechanical behavior from the macroscopic shell to the microscopic electrode material; the traditional constitutive model ignores the influence of crystal orientation and grain boundary effect on material performance, resulting in distorted equivalent parameter calculation; the time domain impact and frequency domain vibration are analyzed separately in load verification, which is difficult to simulate the actual combined working conditions; the fatigue evaluation relies on static load assumption and linear damage model, which cannot capture the nonlinear degradation characteristics of the material and lacks electrochemical-mechanical coupling analysis; resulting in poor simulation accuracy of the battery pack structure strength. SUMMARY

[0003] To solve the above technical problems, a battery pack structure strength simulation test method is provided, which solves the problems of the existing battery pack structure strength simulation mainly adopting a single scale modeling method, which cannot accurately represent the cross-scale mechanical behavior from the macroscopic shell to the microscopic electrode material; the traditional constitutive model ignores the influence of crystal orientation and grain boundary effect on material performance, resulting in distorted equivalent parameter calculation; the time domain impact and frequency domain vibration are analyzed separately in load verification, which is difficult to simulate the actual combined working conditions; the fatigue evaluation relies on static load assumption and linear damage model, which cannot capture the nonlinear degradation characteristics of the material and lacks electrochemical-mechanical coupling analysis; resulting in poor simulation accuracy of the battery pack structure strength.

[0004] To achieve the above purpose, the technical scheme adopted by the application is:

[0005] A battery pack structure strength simulation test method, comprising:

[0006] S1, obtaining the multi-layer structure parameters of the test battery pack, establishing a multi-scale geometric simulation model of the battery pack, and generating multi-scale structure simulation parameters of the test battery pack;

[0007] S2, based on the multi-scale structure simulation parameters of the test battery pack, performing cross-scale constitutive correlation analysis of the test battery pack, and evaluating the equivalent material parameter vector of the test battery pack;

[0008] S3, based on the equivalent material parameter vector of the test battery pack, performing time-frequency domain load verification, establishing a time-frequency domain load coupling equation, and generating a combined load distribution of the test battery pack;

[0009] S4, according to the combined load distribution of the test battery pack, establishing a test battery pack fatigue progressive failure fitting model, and generating a strength damage trend state of the test battery pack.

[0010] Preferably, based on the CAD drawing of the test battery pack, the multi-layer structure parameters of the test battery pack are obtained;

[0011] By using the NURBS surface, the macroscopic analytical method of the test battery pack is established, the geometric discretization and key area local network encryption are carried out for the multi-layer structure parameters of the test battery pack, and the macroscopic finite element network node coordinate appearance thickness distribution of the test battery pack is generated, in the following way:

[0012]

[0013] Wherein, S(u,v) is the appearance thickness distribution of the test battery pack under the macroscopic finite element network node coordinate, P i,j is the finite element network control coordinate point of the test battery pack, N i,A (u) is the A-order spline basis function of the finite element network node coordinate u of the test battery pack, N i,B (v) is the B-order spline basis function of the finite element network node coordinate u of the test battery pack, W i,j is the finite element network control coordinate point weight factor of the test battery pack, n is the total number of index in the u direction of the control point, and m is the total number of index in the v direction of the control point;

[0014] Based on the CT scanning data of the test battery pack, the battery module structure parameters of the test battery pack are obtained;

[0015] Based on the Voronoi grid generation, the stress distribution initial seed points are marked according to the battery module structure parameters of the test battery pack, the macroscopic finite element network node coordinate appearance thickness distribution of the test battery pack is divided, and the mesoscopic finite element grid of the test battery pack is obtained.

[0016] Based on the battery module structure parameters of the test battery pack, the material property value of the mesoscopic finite element grid corresponding unit of the test battery pack is determined, and the local stiffness distribution of the mesoscopic finite grid unit of the test battery pack is established.

[0017] Preferably, according to the SEM scanning, the material microstructure image data of the test battery pack is obtained;

[0018] Based on the Phase-Field phase field method, the local stiffness distribution of the mesoscopic finite grid unit of the test battery pack is associated with the material microstructure image data of the test battery pack, the material phase transition mobility rate of the test battery pack is verified, the local stress influence value of the crystal evolution of the test battery pack is calculated, and the microcrystalline grain boundary energy density of the high stress area of the test battery pack is determined.

[0019] By using the FFT fast Fourier transform, the image texture direction in the material microstructure image data of the test battery pack is analyzed, and the microcrystalline grain orientation distribution of the high stress area of the test battery pack is obtained.

[0020] Preferably, based on the local stiffness distribution of the meso finite grid unit of the test battery pack, the meso geometric size of the test battery pack is determined, and the local stiffness matrix of the meso finite grid unit of the test battery pack is established;

[0021] Using the local stiffness matrix of the meso finite grid unit of the test battery pack, displacement constraints are applied to the corresponding boundaries of the meso finite grid unit of the test battery pack, and the finite element discrete transformation is substituted into the linear algebraic equation system and solved to obtain the characteristic displacement field of the meso finite grid unit of the test battery pack;

[0022] Using the Gauss integral method, the integrand of each unit sampling point in the characteristic displacement field of the meso finite grid unit of the test battery pack is calculated to obtain the equivalent elastic tensor of the meso finite grid unit of the test battery pack, which is converted into engineering constants to obtain the macro-meso correlation homogenization equivalent elastic tensor of the test battery pack.

[0023] Preferably, based on the micro-grain orientation distribution of the high stress area of the test battery pack, the coordinate system of the micro-grain orientation distribution of the high stress area is determined for rotation operation, and the crystal orientation rotation matrix of the test battery pack is established;

[0024] Based on the crystal orientation rotation matrix of the test battery pack, the initial slip direction and the initial slip plane normal of the crystal of the test battery pack are determined, and the crystal slip coefficient of the test battery pack is calculated in the following manner:

[0025]

[0026] Wherein, α is the crystal slip coefficient of the test battery pack, is the initial slip direction of the crystal of the test battery pack, is the initial slip plane normal of the crystal of the test battery pack, and R(θ) is the crystal orientation rotation matrix of the test battery pack;

[0027] Based on the micro-grain boundary energy density of the high stress area of the test battery pack, the initial slip resistance of the crystal of the test battery pack is determined, and the crystal slip strength coefficient of the test battery pack is calculated in the following manner:

[0028]

[0029] Wherein, G α is the crystal slip strength coefficient of the test battery pack, G0 is the initial slip resistance of the crystal of the test battery pack, k is the interface energy coupling coefficient, and γ is the micro-grain boundary energy density coefficient of the high stress area of the test battery pack;

[0030] Determine the local stress tensor of the test battery pack based on the macro-meso associated homogenization equivalent elastic tensor of the test battery pack, and project the macro stress of the crystal slip coefficient of the test battery pack and the crystal slip strength coefficient of the test battery pack to obtain the resolved shear stress of the crystal slip coefficient of the test battery pack;

[0031] Based on the Power-Law model, the resolved shear stress of the crystal slip coefficient of the test battery pack and the crystal slip strength coefficient of the test battery pack are taken as inputs, and the crystal slip shear rate of the test battery pack is taken as output, and the crystal slip shear rate of each test battery pack is superimposed to generate the micro-macro crystal plastic strain rate of the test battery pack;

[0032] Based on the macro-meso associated homogenization equivalent elastic tensor of the test battery pack and the micro-macro crystal plastic strain rate of the test battery pack, the equivalent material parameter vector of the test battery pack is established.

[0033] Preferably, based on the macro finite element network node coordinate appearance thickness distribution of the test battery pack, the shell curvature radius of the test battery pack is initialized;

[0034] Based on the type of the test battery pack, the standardized test shock waveform parameters in the corresponding test standard are screened out to establish the matching test transient force time history curve of the test battery pack;

[0035] According to the inverse ratio between the basic impact frequency of the matching test transient force time history curve and the shell curvature radius of the test battery pack, the shell curvature radius of the test battery pack is updated in real time;

[0036] Based on Morlet wavelet synthesis, the amplitude wavelet function of the test battery pack is established by using the real-time updated initialization shell curvature radius of the test battery pack, the basic impact frequency of the matching test transient force time history curve is verified, the impact force wavelet amplitude is associated with the peak acceleration, and the time-domain impact force time sequence of the test battery pack is generated.

[0037] Preferably, based on the type of the test battery pack, the standardized wideband random vibration signal in the corresponding test standard is screened out;

[0038] According to the direct ratio between the macro-meso associated homogenization equivalent elastic tensor of the test battery pack and the standardized wideband random vibration signal, the basic vibration frequency of the test battery pack is determined;

[0039] According to the basic vibration frequency of the test battery pack, the test battery pack is verified, and the basic vibration frequency frequency domain parameter of the test battery pack is collected;

[0040] Discretize the basic vibration frequency domain parameters of the test battery pack, and synthesize the basic vibration frequency domain time domain signal of the test battery pack;

[0041] Based on the time domain impact force time sequence of the test battery pack and the basic vibration frequency domain time domain signal of the test battery pack, the unit pulse response of the test battery pack under unit time is analyzed through the IRF impact response function;

[0042] According to the unit pulse response of the test battery pack under unit time, the time domain impact force of the test battery pack acts on the basic vibration frequency of the test battery pack, a coupling load time history simulation function is established, a dynamic load spectrum of the test battery pack is generated, and a composite load distribution of the test battery pack is determined.

[0043] Preferably, the linear regression is used to initialize the basic load capacity of the test battery pack according to the multi-layer structure parameters of the test battery pack;

[0044] Based on linear algebra, the basic load capacity of the test battery pack is converted into a vector, and a load vector distribution of the test battery pack is obtained;

[0045] When the composite load distribution of the test battery pack exceeds the load vector distribution of the test battery pack, the moment is recorded as the abnormal moment of the composite load distribution of the test battery pack;

[0046] According to the unit time as the observation window, the overload value at the abnormal moment of the composite load distribution of the test battery pack is taken as the observation object, and the overload characteristic time sequence data at the abnormal moment of the composite load distribution of the test battery pack is collected;

[0047] Based on the SVR linear support regression vector machine, the overload characteristic time sequence data at the abnormal moment of the composite load distribution of the test battery pack is taken as the input, the strength damage quantization hyperplane boundary of the test battery pack is trained, the basic load capacity of the test battery pack is corrected, and the strength damage trend state of the test battery pack is determined.

[0048] Compared with the prior art, the beneficial effects of the present application are:

[0049] The present application provides a battery pack structure strength simulation test scheme, which realizes high-precision characterization of macro-micro structure through multi-scale fusion of NURBS surface and Voronoi grid, establishes equivalent material parameters considering grain boundary evolution by combining crystal plasticity theory and multi-level homogenization algorithm, realizes time-frequency domain coupling simulation of impact-vibration composite load by using Morlet wavelet and IRF function, and finally accurately predicts the gradual failure trend through the SVR dynamic damage model. The present application solves the scale fragmentation and load simplification problems in the traditional method, and improves the simulation test precision of the battery pack structure strength. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 A flow chart of a battery pack structure strength simulation test method. DETAILED DESCRIPTION

[0051] The following description is presented to enable any person skilled in the art to practice the application as claimed. The preferred embodiments disclosed herein are only examples of the application and alternative variations could be adopted by one skilled in the art without departing from the spirit and scope of the application.

[0052] Referring to Figure 1 A battery pack structure strength simulation test method is shown, comprising:

[0053] Step one, obtain the multi-layer structure parameters of the test battery pack, establish a multi-scale geometric simulation model of the battery pack, and generate multi-scale structure simulation parameters of the test battery pack;

[0054] The step one includes the following contents:

[0055] Based on the CAD geometric drawing of the test battery pack, obtain the multi-layer structure parameters of the test battery pack; the multi-layer structure parameters of the test battery pack include: battery pack shell, module frame, cooling plate;

[0056] Using NURBS surface, establish a macroscopic analytical method for the test battery pack, and perform geometric discretization and key area local network encryption on the multi-layer structure parameters of the test battery pack, to generate the macroscopic finite element network node coordinate appearance thickness distribution of the test battery pack, in the following way:

[0057]

[0058] Wherein, S(u,v) is the appearance thickness distribution under the macroscopic finite element network node coordinate of the test battery pack, P i,j is the finite element network control coordinate point of the test battery pack, N i,A (u) is the A-order spline basis function of the finite element network node coordinate u of the test battery pack, N i,B (v) is the B-order spline basis function of the finite element network node coordinate u of the test battery pack, W i,j is the finite element network control coordinate point weight factor of the test battery pack, n is the total number of index in the u direction of the control point, and m is the total number of index in the v direction of the control point;

[0059] Based on the CT scan data of the test battery pack, obtain the battery module structure parameters of the test battery pack; the battery module structure parameters include: battery pack electrode, battery pack separator;

[0060] Based on the Voronoi grid generation, the initial seed points of stress distribution are marked according to the battery module construction parameters of the test battery pack, and the macro finite element network node coordinates and thickness distribution of the test battery pack are divided to obtain the micro finite element grid of the test battery pack;

[0061] As further content, the initial seed points of stress distribution can be marked according to the battery module construction parameters of the test battery pack, and the electrode points of the battery pack can be used as the decision point, or the distribution of the battery pack diaphragm can be used as the decision point, which is well known by those skilled in the art, and will not be described in detail here.

[0062] Based on the battery module construction parameters of the test battery pack, the material attribute values of the micro finite element grid corresponding unit of the test battery pack are determined, and the local stiffness distribution of the micro finite element grid unit of the test battery pack is established.

[0063] According to the SEM scanning, the material microstructure image data of the test battery pack is obtained; the material microstructure image of the test battery pack includes grain size, grain orientation, and grain phase distribution.

[0064] Based on the Phase-Field phase field method, the local stiffness distribution of the micro finite element grid unit of the test battery pack is associated with the material microstructure image data of the test battery pack, the material phase transition mobility rate of the test battery pack is verified, the local stress influence value of the crystal evolution of the test battery pack is calculated, and the micro grain boundary energy density of the high stress area of the test battery pack is determined.

[0065] Using FFT fast Fourier transform, the image texture direction in the material microstructure image data of the test battery pack is analyzed to obtain the micro grain orientation distribution of the high stress area of the test battery pack.

[0066] In use, the contents in the above steps are combined,

[0067] As further content, the traditional battery pack strength simulation method has the problems of ignoring the differences of multi-layer structure in macro single scale modeling, leading to distortion of stress prediction; insufficient geometric discretization and material attribute simplification (such as not considering micro grain boundary and phase change) affecting calculation accuracy; cross-scale data fragmentation in multi-scale modeling, insufficient micro characterization (SEM image lacks quantitative analysis) and low efficiency of phase field method; local stress prediction relies on empirical assumptions, misses the influence of micro grain boundary energy density, and the material phase change response simulation capability under dynamic load is insufficient.

[0068] The scheme realizes high-precision geometric characterization of the battery pack from the macro to the micro through multi-scale fusion modeling of NURBS surface and Voronoi grid, quantitatively correlates microcrystal boundary evolution and macro mechanical properties by combining the Phase-Field phase field method and FFT texture analysis, and accurately locates the high stress area; through automatic transmission of multi-scale parameters and dynamic stiffness updating, the calculation efficiency and dynamic load adaptability are significantly improved, and meanwhile, the modeling is driven by SEM image data, effectively solving the problems of cross-scale fragmentation, insufficient micro characterization and local stress prediction distortion in traditional methods, and providing a more accurate analysis scheme for battery pack structure strength evaluation.

[0069] Step two, based on the multi-scale structure simulation parameters of the test battery pack, performing cross-scale constitutive correlation analysis of the test battery pack to evaluate the equivalent material parameter vector of the test battery pack;

[0070] The step two includes the following contents:

[0071] Based on the local stiffness distribution of the test battery pack, the local stiffness matrix of the test battery pack is established.

[0072] Using the local stiffness matrix of the test battery pack, the displacement constraint is applied to the corresponding boundary of the test battery pack, and the linear algebraic equation group is obtained by substituting the finite element discrete into the linear algebraic equation group and solving, to obtain the characteristic displacement field of the test battery pack.

[0073] Using the Gaussian integral method, the integrand of each sampling point in the characteristic displacement field of the test battery pack is calculated to obtain the equivalent elastic tensor of the test battery pack, which is converted into engineering constant to obtain the macro-micro correlation homogenization equivalent elastic tensor of the test battery pack.

[0074] Based on the microcrystal grain orientation distribution of the high stress area of the test battery pack, the coordinate system of the microcrystal grain orientation distribution of the high stress area is determined to perform rotation operation, and the crystal orientation rotation matrix of the test battery pack is established.

[0075] Based on the crystal orientation rotation matrix of the test battery pack, the crystal initial slip direction and the initial slip face normal of the test battery pack are determined, and the crystal slip coefficient of the test battery pack is calculated in the following manner:

[0076]

[0077] Wherein, α is the crystal slip coefficient of the test battery pack, is the crystal initial slip direction of the test battery pack, is the crystal initial slip face normal of the test battery pack, and R(θ) is the crystal orientation rotation matrix of the test battery pack.

[0078] Determine the crystal initial migration resistance of the test battery pack based on the micrograin boundary energy density of the high stress zone of the test battery pack, calculate the crystal slip intensity coefficient of the test battery pack in the following manner:

[0079]

[0080] Wherein, G α is the crystal slip intensity coefficient of the test battery pack, G0 is the crystal initial migration resistance of the test battery pack, k is the interface energy coupling coefficient, and γ is the micrograin boundary energy density coefficient of the test battery pack;

[0081] Determine the local stress tensor of the test battery pack based on the macro-micro related homogenization equivalent elastic tensor of the test battery pack, and project the macro stress based on the crystal slip coefficient of the test battery pack and the crystal slip intensity coefficient of the test battery pack to obtain the decomposed shear stress of the crystal slip coefficient of the test battery pack.

[0082] Based on the Power-Law model, the decomposed shear stress of the crystal slip coefficient of the test battery pack and the crystal slip intensity coefficient of the test battery pack are taken as inputs, the crystal slip shear rate of the test battery pack is taken as output, and the crystal slip shear rates of each test battery pack are superimposed to generate the micro-macro crystal plastic strain rate of the test battery pack.

[0083] Based on the macro-micro related homogenization equivalent elastic tensor of the test battery pack and the micro-macro crystal plastic strain rate of the test battery pack, the equivalent material parameter vector of the test battery pack is established.

[0084] In use, the contents in the above steps are combined,

[0085] As further content, the traditional battery pack strength simulation method has the following key defects: the homogenization method oversimplifies the macro equivalent parameters, ignores the influence of the microstructure, and causes calculation deviation; the cross-scale constitutive correlation only realizes one-way data transmission, and lacks macro-micro bidirectional coupling; the crystal plasticity model has idealized assumptions of slip system intensity, and does not consider the influence of grain boundary energy density; at the same time, the existing method lacks adaptability to dynamic load, neither considers strain rate effect, nor lacks real-time correlation of micro defect dynamic evolution and slip strength, resulting in inaccurate fatigue prediction.

[0086] The scheme realizes high-precision cross-scale constitutive correlation through multi-level homogenization algorithm and dynamic mapping of crystal orientation, adopts grain boundary energy density to drive slip strength and Power-Law rate sensitivity coupling, and significantly improves the accuracy of the crystal plasticity model; meanwhile, the key defects such as one-way transmission of cross-scale data and idealization of slip strength in traditional methods are solved, realizing bidirectional coupling of macro-micro and accurate prediction under dynamic load, and providing a more accurate solution evaluation method for battery pack structure strength analysis.

[0087] Step three, based on the equivalent material parameter vector of the test battery pack, the time-frequency domain load verification is carried out, the time-frequency domain load coupling equation is established, and the composite load distribution of the test battery pack is generated;

[0088] The step three includes the following contents:

[0089] Based on the macroscopic finite element network node coordinate appearance thickness distribution of the test battery pack, the shell curvature radius of the test battery pack is initialized;

[0090] Based on the type of the test battery pack, the standardized test shock waveform parameters in the corresponding test standard are screened out, and the matching test transient force time history curve of the test battery pack is established;

[0091] According to the matching test transient force time history curve of the test battery pack, the shell curvature radius of the initialized test battery pack is updated in real time according to the inverse ratio between the basic impact frequency of the matching test transient force time history curve and the shell curvature radius of the test battery pack;

[0092] Based on Morlet wavelet synthesis, the shell curvature radius of the initialized test battery pack is used to establish the amplitude wavelet function of the test battery pack, verify the basic impact frequency of the matching test transient force time history curve, correlate the impact force wavelet amplitude with the peak acceleration, and generate the time sequence of the time domain impact force of the test battery pack;

[0093] Based on the type of the test battery pack, the standardized wideband random vibration signal in the corresponding test standard is screened out;

[0094] According to the proportionality between the macroscopic-microscopic associated homogenization equivalent elastic tensor of the test battery pack and the standardized wideband random vibration signal, the basic vibration frequency of the test battery pack is determined;

[0095] The test battery pack is verified according to the basic vibration frequency of the test battery pack, and the basic vibration frequency frequency domain parameter of the test battery pack is collected;

[0096] The basic vibration frequency frequency domain parameter of the test battery pack is discretized and synthesized to generate the basic vibration frequency frequency domain time domain signal of the test battery pack;

[0097] Based on the time-domain impact force time sequence of the test battery pack and the basic vibration frequency frequency-domain time-domain signal of the test battery pack, the unit impulse response of the test battery pack per unit time is analyzed by the IRF impact response function;

[0098] According to the unit impulse response of the test battery pack per unit time, the time-domain impact force of the test battery pack acts on the basic vibration frequency of the test battery pack, a coupling load time history simulation function is established, a dynamic load spectrum of the test battery pack is generated, and a composite load distribution of the test battery pack is determined;

[0099] In use, the above-mentioned contents in the steps are combined,

[0100] As further contents, the conventional battery pack load verification method has the problems that the time-domain impact and the frequency-domain vibration are analyzed separately, the coupling effect is ignored; the standardized waveform is mechanically applied without considering the dynamic adjustment of the geometric characteristics of the battery pack; the fixed basic frequency is used without considering the change of the material stiffness, resulting in distortion of fatigue evaluation; there is no impact-vibration combined simulation mechanism, so that the actual composite working condition cannot be simulated; and there are problems such as rough discretization of frequency domain, loss of high frequency details, static assumption of curvature radius, and ignoring of geometric nonlinearity, which seriously affect the simulation accuracy.

[0101] Step four, according to the composite load distribution of the test battery pack, a fatigue progressive failure fitting model of the test battery pack is established, and a strength damage trend state of the test battery pack is generated;

[0102] The step four includes the following contents:

[0103] According to the multi-layer structure parameters of the test battery pack, the basic load capacity of the test battery pack is initialized by linear regression;

[0104] Based on linear algebra, the basic load capacity of the test battery pack is converted into a vector, and a load vector distribution of the test battery pack is obtained;

[0105] When the composite load distribution of the test battery pack exceeds the load vector distribution of the test battery pack, it is recorded as the abnormal time of the composite load distribution of the test battery pack;

[0106] According to the unit time as the observation window, the overload value at the abnormal time of the composite load distribution of the test battery pack is taken as the observation object, and the overload characteristic time sequence data at the abnormal time of the composite load distribution of the test battery pack is collected;

[0107] Based on the SVR linear support regression vector machine, the overload characteristic time sequence data at the abnormal time of the composite load distribution of the test battery pack is taken as the input, the strength damage quantization hyperplane boundary of the test battery pack is trained, the basic load capacity of the test battery pack is corrected, and the strength damage trend state of the test battery pack is determined;

[0108] In use, in combination with the content in the above steps,

[0109] As further content, the conventional battery pack fatigue evaluation method has the following key defects: the static load assumption is adopted to ignore the material dynamic degradation, leading to early damage missed detection; relying on the rough threshold method to identify abnormal load, the misjudgment rate is high and the time sequence correlation effect cannot be captured; only using the linear regression damage model is difficult to characterize the nonlinear cumulative damage, lacking of electrochemical-mechanical multi-physical field coupling analysis and other problems, leading to insufficient fatigue life prediction accuracy.

[0110] The present scheme adopts support vector regression (SVR) to establish a dynamic damage evaluation model, and realizes high-precision prediction of the gradual failure process of the battery pack through hyperplane boundary correction and intelligent extraction of time sequence characteristics, and effectively integrates macro stress and micro structure evolution characteristics through load vector space mapping and multi-scale damage correlation, solves the problems of static load assumption and linear regression limitation in the conventional method, and provides a more accurate evaluation scheme for the strength state evaluation of the battery pack.

[0111] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection required by the present application is defined by the appended claims and their equivalents.

Claims

1. A method for simulating and testing the structural strength of a battery pack, characterized in that, The method comprises the following steps: S1, obtaining the multi-layer structure parameters of the test battery pack, establishing a multi-scale geometric simulation model of the battery pack, and generating multi-scale structure simulation parameters of the test battery pack, wherein S1 comprises: Based on the CAD drawing of the test battery pack, the multi-layer structure parameters of the test battery pack are obtained; By using NURBS surface, the macroscopic analytical method of the test battery pack is established, the geometric discretization and local network encryption of the key area are carried out for the multi-layer structure parameters of the test battery pack, and the macroscopic finite element network node coordinate thickness distribution of the test battery pack is generated, as follows: , wherein, is the thickness distribution of the battery pack under the macro finite element network node coordinates to be tested, is the finite element network control coordinate point of the battery pack to be tested, is the finite element network node coordinate of the battery pack to be tested is the second spline basis function, is the finite element network node coordinate of the battery pack to be tested is the second spline basis function, is the finite element network control coordinate point weight factor of the battery pack to be tested, is the total number of indexes of the control point in the direction, is the total number of indexes of the control point in the direction; Based on the CT scanning data of the test battery pack, the battery module structure parameters of the test battery pack are obtained; Based on the Voronoi grid generation, the initial stress distribution seed points are marked according to the battery module structure parameters of the test battery pack, and the macro finite element network node coordinate thickness distribution of the test battery pack is divided to obtain the micro finite element grid of the test battery pack; Based on the battery module structure parameters of the test battery pack, the material attribute values of the micro finite element grid corresponding to the test battery pack are determined, and the local stiffness distribution of the micro finite element grid of the test battery pack is established; Wherein, the S1 further comprises: According to the SEM scanning, the material microstructure image data of the test battery pack is obtained; Based on the Phase-Field phase field method, the local stiffness distribution of the micro finite element grid of the test battery pack is associated with the material microstructure image data of the test battery pack, the material phase transition mobility rate of the test battery pack is verified, the crystal evolution local stress influence value of the test battery pack is calculated, and the micro grain boundary energy density of the high stress area of the test battery pack is determined; Using FFT fast Fourier transform, the image texture direction in the material microstructure image data of the test battery pack is analyzed to obtain the micro grain orientation distribution of the high stress area of the test battery pack; S2, based on the multi-scale structure simulation parameters of the test battery pack, the cross-scale constitutive correlation analysis of the test battery pack is carried out, and the equivalent material parameter vector of the test battery pack is evaluated; S3, based on the equivalent material parameter vector of the test battery pack, the time-frequency domain load verification is carried out, the time-frequency domain load coupling equation is established, and the composite load distribution of the test battery pack is generated; S4, according to the composite load distribution of the test battery pack, the test battery pack fatigue progressive failure fitting model is established, and the strength damage trend state of the test battery pack is generated.

2. The method of claim 1, wherein, The S2 comprises: Based on the local stiffness distribution of the micro finite element grid of the test battery pack, the micro geometric size of the test battery pack is determined, and the local stiffness matrix of the micro finite element grid of the test battery pack is established; Using the local stiffness matrix of the micro finite element grid of the test battery pack, the displacement constraint is applied to the corresponding boundary of the micro finite element grid of the test battery pack, the linear algebraic equation group is obtained by substituting the finite element discrete transformation, and the characteristic displacement field of the micro finite element grid of the test battery pack is solved; Using Gauss integral method, the integrand function of each element sampling point in the characteristic displacement field of the micro finite element grid of the test battery pack is calculated to obtain the equivalent elastic tensor of the micro finite element grid of the test battery pack, which is converted into engineering constant to obtain the macro-micro correlation homogenization equivalent elastic tensor of the test battery pack.

3. The method of claim 2, wherein, The S2 further comprises: Based on the micro grain orientation distribution of the high stress area of the test battery pack, the high stress area micro grain orientation distribution coordinate system is determined to perform rotation operation, and the crystal orientation rotation matrix of the test battery pack is established; Based on the crystal orientation rotation matrix of the test battery pack, the crystal initial slip direction and the initial slip face normal of the test battery pack are determined, and the crystal slip coefficient of the test battery pack is calculated in the following manner: , wherein, is a crystal slip coefficient of the battery pack under test, is a crystal initial slip direction of the battery pack under test, is a crystal initial slip plane normal of the battery pack under test, is a crystal orientation rotation matrix of the battery pack under test; Based on the micrograin boundary energy density of the high stress area of the test battery pack, the crystal initial migration resistance of the test battery pack is determined, and the crystal slip strength coefficient of the test battery pack is calculated in the following manner: , wherein, is a coefficient of the crystal slip strength of the battery pack to be tested, is a coefficient of the initial crystal migration resistance of the battery pack to be tested, is a coefficient of the interfacial energy coupling, is a coefficient of the micrograin boundary energy density of the high stress zone of the battery pack to be tested; Determine the local stress tensor of the test battery pack based on the macro-meso correlated homogenization equivalent elastic tensor of the test battery pack, and project the macro stress of the test battery pack on the crystal slip coefficient and the crystal slip strength coefficient of the test battery pack to obtain the resolved shear stress of the crystal slip coefficient of the test battery pack; Based on the Power-Law model, the resolved shear stress of the crystal slip coefficient of the test battery pack and the crystal slip strength coefficient of the test battery pack are taken as inputs, and the crystal slip shear rate of the test battery pack is taken as output, and the crystal slip shear rate of each test battery pack is superimposed to generate the micro-macro crystal plastic strain rate of the test battery pack; Based on the macro-meso correlated homogenization equivalent elastic tensor of the test battery pack and the micro-macro crystal plastic strain rate of the test battery pack, the equivalent material parameter vector of the test battery pack is established.

4. The method of claim 3, wherein, The S3 includes: Initialize the shell curvature radius of the test battery pack based on the macro finite element network node coordinate appearance thickness distribution of the test battery pack; Based on the type of the test battery pack, the standardized test shock waveform parameters in the corresponding test standard are screened out to establish the matching test transient force time history curve of the test battery pack; According to the inverse ratio between the basic impact frequency of the matching test transient force time history curve and the shell curvature radius of the test battery pack, the shell curvature radius of the test battery pack is updated in real time; Based on Morlet wavelet synthesis, the amplitude wavelet function of the test battery pack is established by using the real-time updated shell curvature radius of the test battery pack, the basic impact frequency of the matching test transient force time history curve is verified, the impact force wavelet amplitude is associated with the peak acceleration, and the time sequence of the time sequence of the test battery pack is generated.

5. The method of claim 4, wherein, The S3 also includes: Based on the type of the test battery pack, the standardized wideband random vibration signal in the corresponding test standard is screened out; According to the direct ratio between the macro-meso correlated homogenization equivalent elastic tensor of the test battery pack and the standardized wideband random vibration signal, the basic vibration frequency of the test battery pack is determined; According to the basic vibration frequency of the test battery pack, the basic vibration frequency frequency domain parameter of the test battery pack is collected; Discretization processing is performed on the basic vibration frequency frequency domain parameter of the test battery pack, and the basic vibration frequency frequency domain time domain signal of the test battery pack is synthesized; Based on the time sequence of the time sequence of the test battery pack and the basic vibration frequency frequency domain time domain signal of the test battery pack, the unit pulse response of the test battery pack under unit time is analyzed by IRF impact response function; According to the unit pulse response of the test battery pack under unit time, the time domain impact force of the test battery pack acts on the basic vibration frequency of the test battery pack, a coupled load time history simulation function is established, a dynamic load spectrum of the test battery pack is generated, and a composite load distribution of the test battery pack is determined.

6. The method of claim 5, wherein, The S4 includes: Initialize the basic load capacity of the test battery pack according to the multi-layer structure parameters of the test battery pack by linear regression; Based on linear algebra, the basic load capacity of the test battery pack is converted into a vector to obtain the load vector distribution of the test battery pack; The moment when the composite load distribution of the screening test battery package exceeds the load vector distribution of the test battery package is recorded as the moment when the composite load distribution of the test battery package is abnormal; According to the unit time as the observation window, the overload value at the moment when the composite load distribution of the test battery package is abnormal is taken as the observation object, and the overload feature time series data at the moment when the composite load distribution of the test battery package is abnormal is collected; Based on the SVR linear support regression vector machine, the overload feature time series data at the moment when the composite load distribution of the test battery package is abnormal is taken as the input, the strength damage quantization hyperplane boundary of the test battery package is trained, the basic load capacity of the initialized test battery package is corrected, and the strength damage trend state of the test battery package is determined.

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