A battery pack system mechanical safety prediction method

Abstract

The application discloses a battery pack system mechanical safety prediction method, comprising the following steps: 1) obtaining the mechanical response of a battery pack system and the deformation information of each battery monomer under different extrusion speeds, extrusion positions and the thickness of core components of the battery pack system, and constructing a battery pack system mechanical safety prediction sample set; 2) training a machine learning model by using the battery pack system mechanical safety prediction sample set, and obtaining a battery pack mechanical safety performance prediction model; and 3) building the mechanical response of the battery pack system and the deformation information of each battery monomer under different extrusion speeds, extrusion positions and the thickness of core components of the battery pack system by using the battery pack mechanical safety performance prediction model, and building a fitting curved surface between the core components and the mechanical information of the battery pack system, a module or monomers. When a vehicle is extruded, the mechanical safety performance of the battery pack system can be predicted in time and efficiently.

Description

Technical Field

[0001] The present invention relates to the field of electric vehicle safety and vehicle battery pack system design, and in particular to a method for predicting the mechanical safety of a battery pack system. Background Art

[0002] With the rapid development of my country's automobile industry, the number of electric vehicles in China has ranked first in the world. As a key core component of electric vehicles, the battery pack system plays a vital role in power supply. However, given the harsh road environment and increasingly complex traffic environment, different extrusion conditions (such as vehicle collisions, battery pack scratches, obstacle impacts, etc.) can cause immeasurable damage to the battery pack system. In severe cases, they may even cause safety accidents such as fires and explosions, greatly affecting the driving safety and traffic safety of electric vehicles. In addition, if stress analysis of the battery pack system under extrusion conditions is not performed, it will be impossible to evaluate the reliability of the battery pack system after micro-extrusion, which will pose a safety hazard to the continued use of the battery pack and vehicle driving in the future.

[0003] Mechanical safety prediction of battery pack systems can promptly identify potential mechanical structural defects and weaknesses. For example, this assessment can determine the battery pack's ability to withstand various compression conditions, preventing damage from vehicle collisions, scrapes, or impacts with obstacles, thereby reducing the risk of serious accidents such as fire and explosion. This ensures that the battery pack maintains stable structural integrity under various complex driving conditions, providing a strong guarantee for the safe operation of electric vehicles.

[0004] In the event of an electric vehicle accident, good battery pack mechanical safety can reduce the risk of battery system damage to passengers. If the battery pack remains stable during a collision, preventing rupture or leakage, it can reduce the risk of electric shock and burns. Furthermore, a reliable battery pack structure helps maintain the overall stability of the vehicle, reducing the likelihood of secondary accidents.

[0005] Therefore, it is necessary to propose a reliable mechanical safety detection scheme for battery pack systems. Summary of the Invention

[0006] The purpose of the present invention is to provide a method for predicting the mechanical safety of a battery pack system, comprising the following steps:

[0007] 1) Establish a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules;

[0008] 2) Set the extrusion speed, extrusion position, and thickness of the core components of the battery pack system, and conduct extrusion simulation analysis of the battery pack system. Use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and core component thicknesses;

[0009] 3) The obtained module deformation is introduced into the multi-body dynamics model, and the deformation information of each battery cell is obtained through dynamics simulation;

[0010] 4) Steps 2) to 3) are repeated n times to obtain n sets of battery pack system mechanical response and deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system, and a battery pack system mechanical safety prediction sample set is constructed;

[0011] 5) The battery pack system mechanical safety prediction sample set is used to train the machine learning model to obtain a battery pack mechanical safety performance prediction model;

[0012] 6) The battery pack mechanical safety performance prediction model is used to build the mechanical response of the battery pack system and the deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system.

[0013] Further, in step 1), the steps of establishing the complete finite element model of the battery pack system and the multi-body dynamics model of the battery pack system module at different SOC include:

[0014] 1.1) Establish a finite element model of the battery pack shell;

[0015] 1.2) Establish a finite element model of the battery module;

[0016] 1.3) According to the real connection relationship of the battery pack system module, integrate the finite element model of the battery pack shell and the finite element model of the battery module, thereby establishing a complete finite element model of the battery pack system including the contact connection relationship.

[0017] 1.4) Build a multi-body dynamics model of the battery pack system module at different SOC by equivalent the battery and the filling glue to a spring damping structure; wherein the stiffness and damping parameters are used to represent the battery SOC.

[0018] Further, in step 1.1), the steps of establishing the finite element model of the battery pack shell include: according to the size, structure and material parameters of the battery pack system shell, defining the battery pack system shell parameters in the finite element commercial software, thereby establishing the finite element model of the battery pack shell.

[0019] Further, the battery pack system shell parameters include unit type, size parameter, thickness parameter and material parameter.

[0020] Further, in step 1.2), the steps of establishing the finite element model of the battery module include:

[0021] 1.2.1) According to the size parameters of the battery module, a geometric model of the battery module is established;

[0022] 1.2.2) Homogenize the battery module materials, define the module material parameters, and establish a finite element model of the battery module.

[0023] Furthermore, the core components include a long bracket, a lifting lug, a bottom shell, a lower supporting beam, upper and lower connecting brackets and an upper bracket that are not in the existing battery pack system.

[0024] Furthermore, the battery pack mechanical safety performance prediction model takes the extrusion speed, extrusion position and thickness of the core components of the battery pack system as input, and takes the mechanical response of the battery pack system and the deformation information of each battery cell as output.

[0025] Furthermore, the hyperparameters of the battery pack mechanical safety performance prediction model are determined through random experiments; the hyperparameters include learning rate and number of iterations.

[0026] Furthermore, machine learning models include but are not limited to deep neural network models, particle swarm optimization models, condor optimization-extreme learning machines, and random forest models.

[0027] Furthermore, the mean squared error loss function of the machine learning model is As shown below:

[0028]

[0029] Where, y represents the output value and true value of the machine learning model respectively; n is the number of samples.

[0030] The technical effect of the present invention is unquestionable. The present invention establishes a complete finite element model of the battery pack system based on the actual structure of the battery pack system, obtains the mechanical information of the battery pack system and the module based on finite element extrusion analysis, builds a multi-body dynamic model of the module through an equivalent model, uses the maximum deformation of the module obtained by finite element analysis, and obtains the deformation information of the monomer through dynamic simulation, thereby completing the calculation of the maximum stress, maximum deformation, maximum acceleration of the battery pack system and module and the maximum deformation of the monomer under different extrusion speeds, different extrusion positions, different SOCs and different core component thicknesses. Combined with machine learning methods, limited data is used to efficiently predict the mechanical response of the system under all extrusion conditions and component thickness combinations. A fitting surface is also built between the core components and the mechanical information of the battery pack system, module or monomer to guide R&D personnel in design.

[0031] The present invention combines finite element modeling and analysis of the battery pack system, multi-body dynamics modeling and analysis, and machine learning methods to efficiently predict the mechanical safety of the battery pack system under different extrusion speeds, different extrusion positions, different SOCs, and different core component thickness combinations. When a vehicle is squeezed, the present invention can timely and efficiently predict the mechanical safety performance of the battery pack system, which can provide a reference for the timely response of the occupants and ensure the safety of the passengers' lives and property. In addition, the present invention reveals the impact of changes in the thickness of core components on the mechanical safety performance of the battery pack system, which can provide a reference for researchers' design. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a method for predicting mechanical safety of an efficient battery pack system;

[0033] Figure 2 The finite element model and multi-body dynamics equivalent diagram of the battery pack system;

[0034] Figure 3 A comparison chart of the prediction accuracy of different machine learning models;

[0035] Figure 4 The figure is a fitted surface diagram of the thickness of the bottom shell and upper cover of the battery pack system and the maximum deformation of the module. DETAILED DESCRIPTION

[0036] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0037] Example 1:

[0038] See also Figures 1 to 4 , a battery pack system mechanical safety prediction method, comprising the following steps:

[0039] 1) Establish a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules;

[0040] 2) Set the extrusion speed, extrusion position, and thickness of the core components of the battery pack system, and conduct extrusion simulation analysis of the battery pack system. Use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and core component thicknesses;

[0041] 3) Importing the obtained module deformation into the multi-body dynamics model, and obtaining the deformation information of each battery cell through dynamic simulation;

[0042] 4) Repeat steps 2) to 3) n times to obtain n sets of mechanical response of the battery pack system and deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system, and construct a mechanical safety prediction sample set of the battery pack system;

[0043] 5) Train the machine learning model using the mechanical safety prediction sample set of the battery pack system to obtain a mechanical safety performance prediction model of the battery pack;

[0044] 6) Use the mechanical safety performance prediction model of the battery pack to build the mechanical response of the battery pack system and the deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system, and build a fitting surface between the core components and the mechanical information of the battery pack system, module or cell based on the information.

[0045] In step 1), the steps of establishing the complete finite element model of the battery pack system and the multi-body dynamics model of the battery pack system module with different SOC include:

[0046] 1.1) Establish a finite element model of the battery pack shell;

[0047] 1.2) Establish a finite element model of the battery module;

[0048] 1.3) According to the real connection relationship of the battery pack system module, integrate the finite element model of the battery pack shell and the finite element model of the battery module, thereby establishing a complete finite element model of the battery pack system containing the contact connection relationship.

[0049] 1.4) Build a multi-body dynamics model of the battery pack system module with different SOC by equivalent the battery and the filling glue to a spring damping structure; wherein the stiffness and damping parameters are used to represent the SOC of the battery.

[0050] In step 1.1), the steps of establishing the finite element model of the battery pack shell include: defining the parameters of the battery pack shell in the finite element commercial software according to the size, structure and material parameters of the battery pack shell, thereby establishing the finite element model of the battery pack shell.

[0051] The parameters of the battery pack shell include unit type, size parameter, thickness parameter and material parameter.

[0052] In step 1.2), the steps of establishing the finite element model of the battery module include:

[0053] 1.2.1) Establish a geometric model of the battery module according to the size parameters of the battery module;

[0054] 1.2.2) Homogenize the material of the battery module, define the material parameters of the module, and establish the finite element model of the battery module.

[0055] The core components include but are not limited to the long bracket, lifting ears, bottom shell, lower supporting beam, upper and lower connecting brackets and upper bracket of the existing battery pack system.

[0056] The battery pack mechanical safety performance prediction model takes the extrusion speed, extrusion position and thickness of the core components of the battery pack system as input, and takes the mechanical response of the battery pack system and the deformation information of each battery cell as output.

[0057] The hyperparameters of the battery pack mechanical safety performance prediction model are determined through random experiments; the hyperparameters include learning rate and number of iterations.

[0058] Machine learning models include but are not limited to deep neural network models, particle swarm optimization models, condor optimization-extreme learning machine and random forest models.

[0059] Mean Squared Error Loss Function for Machine Learning Models As shown below:

[0060]

[0061] Where, y represents the output value and true value of the machine learning model respectively; n is the number of samples.

[0062] Example 2:

[0063] A method for predicting mechanical safety of a battery pack system comprises the following steps:

[0064] 1) Establish a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules;

[0065] 2) Set the extrusion speed, extrusion position, and thickness of the core components of the battery pack system, and conduct extrusion simulation analysis of the battery pack system. Use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and core component thicknesses;

[0066] 3) Importing the obtained module deformation into the multi-body dynamics model, and obtaining the deformation information of each battery cell through dynamic simulation;

[0067] 4) Repeat steps 2) to 3) n times to obtain n sets of mechanical responses of the battery pack system and deformation information of each battery cell under different extrusion speeds, extrusion positions, and thicknesses of core components of the battery pack system, and construct a sample set of mechanical safety predictions for the battery pack system;

[0068] 5) Use the battery pack system mechanical safety prediction sample set to train the machine learning model to obtain the battery pack mechanical safety performance prediction model;

[0069] 6) Use the battery pack mechanical safety performance prediction model to build the mechanical response of the battery pack system and the deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system, and build a fitting surface between the core components and the mechanical information of the battery pack system, module or cell.

[0070] Example 3:

[0071] A method for predicting mechanical safety of a battery pack system, with the same technical content as in Example 2, further comprising the steps of establishing a complete finite element model of the battery pack system and a multi-body dynamics model of battery pack system modules with different SOCs in step 1), comprising:

[0072] 1.1) Establish a finite element model of the battery pack shell;

[0073] 1.2) Establish a finite element model of the battery module;

[0074] 1.3) Based on the actual connection relationship of the battery pack system module, the battery pack shell finite element model and the battery module finite element model are integrated to establish a complete battery pack system finite element model including the contact connection relationship.

[0075] 1.4) A multi-body dynamics model of battery pack system modules with different SOCs is constructed by equating the battery and filler to a spring-damper structure; the stiffness and damping parameters are used to characterize the battery SOC.

[0076] Example 4:

[0077] A method for predicting the mechanical safety of a battery pack system, the technical content of which is the same as any one of Examples 2-3. Furthermore, in step 1.1), the step of establishing a finite element model of the battery pack shell includes: defining the battery pack system shell parameters in a finite element commercial software according to the size, structure and material parameters of the battery pack system shell, thereby establishing a finite element model of the battery pack shell.

[0078] Example 5:

[0079] A method for predicting the mechanical safety of a battery pack system, the technical content of which is the same as any one of Examples 2-4, and further, the battery pack system shell parameters include unit type, size parameters, thickness parameters, and material parameters.

[0080] Example 6:

[0081] A method for predicting mechanical safety of a battery pack system, having the same technical content as any one of Examples 2-5, further comprising the step of establishing a finite element model of the battery module in step 1.2) comprising:

[0082] 1.2.1) Establish a geometric model of the battery module based on the dimensional parameters of the battery module;

[0083] 1.2.2) Homogenize the battery module materials, define the module material parameters, and establish a finite element model of the battery module.

[0084] Example 7:

[0085] A method for predicting the mechanical safety of a battery pack system, the technical content of which is the same as any one of Examples 2-6. Furthermore, the core components include a long bracket, a lifting lug, a bottom shell, a lower supporting beam, upper and lower connecting brackets, and an upper bracket of the battery pack system.

[0086] Example 8:

[0087] A method for predicting the mechanical safety of a battery pack system, having the same technical content as any one of Examples 2-7. Furthermore, the battery pack mechanical safety performance prediction model uses the extrusion speed, extrusion position and thickness of the core components of the battery pack system as inputs, and uses the mechanical response of the battery pack system and the deformation information of each battery cell as output.

[0088] Example 9:

[0089] A method for predicting the mechanical safety of a battery pack system, the technical content of which is the same as any one of Examples 2-8, further, the hyperparameters of the battery pack mechanical safety performance prediction model are determined through random experiments; the hyperparameters include a learning rate and the number of iterations.

[0090] Example 10:

[0091] A method for predicting the mechanical safety of a battery pack system, having the same technical content as any one of Examples 2-9. Furthermore, the machine learning model includes but is not limited to a deep neural network model, a particle swarm optimization model, a vulture optimization-extreme learning machine, and a random forest model.

[0092] Example 11:

[0093] A method for predicting mechanical safety of a battery pack system, the technical content of which is the same as any one of Examples 2-10, furthermore, the mean square error loss function of the machine learning model As shown below:

[0094]

[0095] Where, y represents the output value and true value of the machine learning model respectively; n is the number of samples.

[0096] Example 12:

[0097] An efficient battery pack system mechanical safety prediction method includes the following steps:

[0098] 1) Establish a complete finite element model of the battery pack system.

[0099] The steps of establishing a complete finite element model of the battery pack system include:

[0100] 1.1) Establish a finite element model of the battery pack shell;

[0101] 1.2) Establish a finite element model of the battery module;

[0102] 1.3) According to the real connection relationship of the battery pack system module, a complete finite element model of the battery pack system is established, which includes the contact connection relationship.

[0103] 1.4) By equivalent the battery and the filling glue to the spring damping structure to build the multi-body dynamics model of the module, different battery SOC corresponds to different stiffness and damping parameters.

[0104] 2) Select different extrusion speeds, extrusion positions, and battery pack system core component thicknesses to carry out battery pack system extrusion simulation analysis, and use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and core component thicknesses;

[0105] 3) The obtained module deformation is imported into the multi-body dynamics model, and the deformation information of each battery monomer is obtained through dynamics simulation.

[0106] The main steps are:

[0107] 3.1) After obtaining the mechanical information of the battery pack system by using finite element analysis, the deformation of the module is input into the multi-body dynamics model;

[0108] 3.2) Through dynamics simulation, the deformation information of the monomer can be quickly obtained;

[0109] 3.3) Combined with the finite element analysis results, the maximum stress, maximum deformation, maximum acceleration of the battery pack system and module, and the maximum deformation of the monomer under different extrusion positions, different extrusion speeds and different SOC can be obtained, and then the data set is obtained.

[0110] 4) Use the extrusion speed, extrusion position, SOC, battery pack system core component thickness, and the mechanical information data set of the battery pack system, module or monomer to establish a machine learning model. When establishing the model, the data set is divided into training set and test set according to a certain proportion. For example, 400 groups of data (80%, but not limited to) and 100 groups of data (20%, but not limited to) are extracted from 500 groups of data as the training set and test set of the machine learning model. The final goal is to establish the optimal mechanical safety prediction model of the battery pack system.

[0111] 5) Using the established machine learning model, accurately predict the mechanical safety performance of the battery pack system under different extrusion conditions and with different thicknesses of the battery pack system core components;

[0112] 6) Build fitting surfaces between core components and battery pack systems, modules or single-cell mechanical information to guide R&D personnel in design.

[0113] Example 13:

[0114] A method for predicting mechanical safety of a battery pack system comprises the following steps:

[0115] 1) Establish a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules;

[0116] 2) Conduct extrusion simulation analysis of the battery pack system by selecting different extrusion speeds, extrusion positions, and thicknesses of core components of the battery pack system, and use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and thicknesses of core components;

[0117] 3) Importing the obtained module deformation into the multi-body dynamics model, and obtaining the deformation information of each battery cell through dynamic simulation;

[0118] 4) Build a machine learning model using a dataset of extrusion speed, extrusion position, SOC, thickness of core components of the battery pack system, and mechanical information of the battery pack system, module, or cell;

[0119] 5) Use the established machine learning model to accurately predict the mechanical safety performance of the battery pack system under different extrusion conditions;

[0120] 6) Build fitting surfaces between core components and battery pack systems, modules or single-cell mechanical information to guide R&D personnel in design.

[0121] The steps to build a complete finite element model of the battery pack system include:

[0122] 1.1) Establish a finite element model of the battery pack shell;

[0123] 1.2) Establish a finite element model of the battery module;

[0124] 1.3) Based on the actual connection relationship of the battery pack system module, a complete battery pack system finite element model including the contact connection relationship is established.

[0125] 1.4) A multi-body dynamics model of the module is constructed by equating the battery and the filling glue to a spring-damper structure. Different battery SOCs correspond to different stiffness and damping parameters.

[0126] The working condition analyzed is the extrusion of the battery pack system (the loading method refers to the national standard GB38031-2020).

[0127] Finite element analysis is used to obtain mechanical information about the battery pack system, and the module deformation is then input into the multibody dynamics model. Dynamic simulation allows for rapid acquisition of cell deformation information. Combined with the finite element analysis results, the maximum stress, maximum deformation, maximum acceleration, and maximum cell deformation of the battery pack system and module can be determined at different extrusion positions, extrusion speeds, and SOCs.

[0128] A machine learning model is established using the extrusion speed, extrusion position, SOC, thickness of core components of the battery pack system, and mechanical information datasets of the battery pack system, module, or cell to reveal the complex nonlinear relationship between extrusion speed, extrusion position, SOC, thickness of core components of the battery pack system, and mechanical information of the battery pack system, module, or cell.

[0129] The machine learning model developed takes extrusion speed, extrusion position, SOC, and thickness parameters of core battery pack components as inputs, and outputs mechanical information parameters of the battery pack system, modules, or cells. Other hyperparameters in the model, such as the learning rate and number of iterations, can be determined through (but not limited to) randomized experiments. The ultimate goal is to identify the optimal machine learning parameters and establish an optimal mechanical safety prediction model for the battery pack system.

[0130] Example 14:

[0131] See also Figures 1 to 4 , an efficient battery pack system mechanical safety prediction method, comprising the following steps:

[0132] The purpose of the present invention is to provide an efficient method for predicting the mechanical safety of a battery pack system, comprising the following steps:

[0133] 1. Establish a complete finite element model of the battery pack system. By equating the battery and filler to a spring-damper structure, a multi-body dynamics model of the module is constructed. Different battery SOCs correspond to different stiffness and damping parameters.

[0134] The steps to build a complete finite element model of the battery pack system include:

[0135] 1.1) Based on the dimensions, structure, and material parameters of a battery pack system housing for an electric vehicle, define the unit type, dimensional parameters, thickness parameters, material parameters, etc. in commercial finite element software and establish a finite element model of the battery pack housing;

[0136] 1.2) Establish a geometric model of the battery module based on the dimensional parameters of the battery module, homogenize the battery module material, define the module material parameters, and establish a finite element model of the battery module;

[0137] 1.3) Based on the actual connection relationship of the battery pack system module, the contact connection relationships such as welding and friction between the different components of the battery pack system and the battery module are established in the finite element commercial software. The battery pack shell finite element model and the battery module finite element model are coupled to establish a complete battery pack system finite element model including the contact connection relationship.

[0138] Figure 2 It is the finite element model and multi-body dynamics equivalent diagram of the battery pack system.

[0139] 2. Select different extrusion speeds, extrusion positions, and thicknesses of core components of the battery pack system to conduct extrusion simulation analysis of the battery pack system, and use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and thicknesses of core components. The complete finite element model of the battery pack system is universal and can be used for complex mechanical analysis in different finite element commercial software (such as LS-DYNA or ABAQUS):

[0140] 3. After using finite element analysis to obtain the mechanical information of the battery pack system, the deformation of the module is input into the multi-body dynamics model. The multi-body dynamics model is universal and can be used in different commercial dynamics software (such as ADAMS). The deformation information of the single cell can be quickly obtained through dynamic simulation. Combined with the finite element analysis results, the maximum stress, maximum deformation, maximum acceleration and maximum deformation of the battery pack system and module under different extrusion positions, different extrusion speeds, different SOCs and different combinations of the bottom shell and upper cover thickness of the battery pack system can be obtained to form a data set.

[0141] 4. Build a machine learning model using a dataset of mechanical information including extrusion speed, extrusion position, SOC, thickness of core components of the battery pack system, and the battery pack system, modules, or cells. When building the model, divide the dataset into training and test sets according to a specific ratio. For example, if 500 sets of data are obtained, 400 sets of data (80%, but not limited to) and 100 sets of data (20%, but not limited to) can be extracted from the 500 sets of data as the training and test sets for the machine learning model, respectively. The ultimate goal is to establish an optimal mechanical safety prediction model for the battery pack system.

[0142] 5. Use the established machine learning model to accurately predict the mechanical safety performance of the battery pack system under different extrusion conditions and with different thicknesses of the battery pack system core components;

[0143] Figure 3 A comparison chart of the prediction accuracy of different machine learning models.

[0144] 6. Build the fitting surface between the core components and the battery pack system, the module or the single mechanical information, guide the researchers to design.

[0145] Figure 4 The fitting surface of the battery pack system bottom shell and the upper cover thickness and the maximum deformation of the module.

[0146] Experimental results:

[0147] In Figure 3 , four kinds of machine learning algorithms, including deep neural network (DNN), particle swarm optimization-radial basis function (PSO-RBF), bald eagle optimization-extreme learning machine (BES-ELM) and random forest (RF), are used, R 2 is the evaluation index of model accuracy, and four kinds of machine learning algorithms are used to predict the maximum deformation of the module, the maximum mises stress of the module and the maximum deformation of the single body respectively. From the results, the prediction accuracy of the four models is above 0.9, and the DNN model has the highest prediction accuracy, which proves that machine learning algorithms can be used to predict the mechanical safety performance of the battery pack system with high accuracy.

[0148] In Figure 4 , the fitting relationship between the battery pack system bottom shell thickness (t_bottom), the upper cover thickness (t_upper) and the maximum deformation of the module (Max deformation-module) shows that the reduction of the battery pack system bottom shell and the reduction of the battery pack system upper cover thickness can help to reduce the mechanical response of the battery pack system under compression and improve the safety performance of the battery pack system.

[0149] In summary, this embodiment combines the finite element modeling and analysis of the battery pack system, the multi-body dynamics modeling and analysis, and the machine learning method to efficiently and accurately predict the mechanical safety of the battery pack system under different compression speeds, different compression positions, different SOC and different core component thickness combinations. When the vehicle is compressed, timely and efficient mechanical safety performance prediction of the battery pack system can provide reference for timely response of passengers and ensure the safety of passengers' life and property. In addition, the influence of core component thickness change on the mechanical safety performance of the battery pack system is revealed, which can provide reference for researchers' design.

Claims

1. A battery pack system mechanical safety prediction method, characterized in that: The following steps are involved: 1) Establish a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules; 2) Set the extrusion speed, extrusion position, and thickness of the core components of the battery pack system, and conduct extrusion simulation analysis of the battery pack system. Use the finite element model to obtain the mechanical response of the battery pack system under different extrusion speeds, extrusion positions, and core component thicknesses; 3) Importing the obtained module deformation into the multi-body dynamics model, and obtaining the deformation information of each battery cell through dynamic simulation; 4) Repeat steps 2) to 3) n times to obtain n sets of mechanical responses of the battery pack system and deformation information of each battery cell under different extrusion speeds, extrusion positions, and thicknesses of core components of the battery pack system, and construct a sample set of mechanical safety predictions for the battery pack system; 5) Use the battery pack system mechanical safety prediction sample set to train the machine learning model to obtain the battery pack mechanical safety performance prediction model; 6) Use the battery pack mechanical safety performance prediction model to build the mechanical response of the battery pack system and the deformation information of each battery cell under different extrusion speeds, extrusion positions and thicknesses of the core components of the battery pack system.

2. A battery pack system mechanical safety prediction method according to claim 1, characterized in that: In step 1), the steps of establishing a complete finite element model of the battery pack system and a multi-body dynamics model of different SOC battery pack system modules include: 1.1) Establish a finite element model of the battery pack shell; 1.2) Establish a finite element model of the battery module; 1.3) Based on the actual connection relationship of the battery pack system module, the battery pack shell finite element model and the battery module finite element model are integrated to establish a complete battery pack system finite element model including the contact connection relationship; 1.4) A multi-body dynamics model of battery pack system modules with different SOCs is constructed by equating the battery and filler to a spring-damper structure; the stiffness and damping parameters are used to characterize the battery SOC.

3. A battery pack system mechanical safety prediction method according to claim 2, characterized in that: In step 1.1), the step of establishing a finite element model of the battery pack shell includes: defining the battery pack system shell parameters in a finite element commercial software according to the size, structure and material parameters of the battery pack system shell, thereby establishing a finite element model of the battery pack shell.

4. A battery pack system mechanical safety prediction method according to claim 3, characterized in that: The battery pack system shell parameters include unit type, size parameters, thickness parameters, and material parameters.

5. The method for predicting mechanical safety of a battery pack system according to claim 2, characterized in that: In step 1.2), the steps of establishing the battery module finite element model include: 1.2.1) Establish a geometric model of the battery module based on the dimensional parameters of the battery module; 1.2.2) Homogenize the battery module materials, define the module material parameters, and establish a finite element model of the battery module.

6. A battery pack system mechanical safety prediction method according to claim 1, characterized in that: The core components include but are not limited to the long bracket, lifting ears, bottom shell, lower supporting beam, upper and lower connecting brackets and upper bracket of the battery pack system.

7. The method for predicting mechanical safety of a battery pack system according to claim 1, characterized in that: The battery pack mechanical safety performance prediction model takes the extrusion speed, extrusion position and thickness of the core components of the battery pack system as input, and takes the mechanical response of the battery pack system and the deformation information of each battery cell as output.

8. The method for predicting mechanical safety of a battery pack system according to claim 1, characterized in that: The hyperparameters of the battery pack mechanical safety performance prediction model are determined through random experiments; the hyperparameters include learning rate and number of iterations.

9. The method for predicting mechanical safety of a battery pack system according to claim 1, characterized in that: Machine learning models include but are not limited to deep neural network models, particle swarm optimization models, condor optimization-extreme learning machine and random forest models.

10. A battery pack system mechanical safety prediction method according to claim 9, characterized in that: Mean Squared Error Loss Function for Machine Learning Models As shown below: Where, y represents the output value and true value of the machine learning model respectively; n is the number of samples.

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