CN115408900B - Method for optimizing extrusion stress and vibration fatigue life of battery pack system - Google Patents
Method for optimizing extrusion stress and vibration fatigue life of battery pack system Download PDFInfo
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Abstract
The invention discloses a method for optimizing extrusion stress and vibration fatigue life of a battery pack system, which comprises the following steps: 1) Setting the thickness of a finite element model part of a battery pack system; 2) Testing the system extrusion stress of the finite element model of the battery pack system under different thickness combinations; 3) Testing the fatigue life of the finite element model of the battery pack system under the combination of different thicknesses; 4) Modifying the thickness of the finite element model part of the battery pack system, and returning to the step 2) -step 3), and obtaining the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack system; 6) Constructing a characterization model of extrusion stress and fatigue life; 7) And (3) obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by utilizing a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thickness of the battery pack system component. The invention solves the multi-objective optimization problem of the mechanical response of the battery pack system under the extrusion working condition and the vibration working condition.
Description
Technical Field
The invention relates to the field of electric automobiles, in particular to a method for optimizing extrusion stress and vibration fatigue life of a battery pack system.
Background
The battery pack system is used as a key core component on the electric automobile and plays a crucial role in power supply. Because the driving road environment is severe, the traffic environment is increasingly complex, different mechanical conditions (such as vehicle collision, battery pack vibration, obstacle impact and the like) can cause damage to the battery pack system which is difficult to estimate, and safety accidents such as fire and explosion can occur when the battery pack system is severe, so that the driving safety and the traffic safety of the electric automobile are greatly influenced. In addition, if the extrusion is not performed, the reliability of the battery pack system after vibration cannot be evaluated by performing stress analysis on the battery pack system under the vibration working condition, and potential safety hazards are left for the continuous use of future battery packs and the running of vehicles.
The battery pack system is a power source of a pure electric vehicle and a hybrid electric vehicle and generally comprises a lower bottom shell, an upper cover, a battery module, longitudinal beams/edges, cross beams/edges, module mounting plates, lifting lugs, long/short brackets, reinforcing plates and the like. For a battery pack system of defined structure, its safety performance is mainly determined by the thickness and material parameters of the critical components. If different battery pack samples are manufactured by changing thickness parameters of different components, experimental analysis is performed to study the safety under the vibration working condition, and the time cost and the economic cost are very high. Therefore, the method of combining finite element simulation and deep learning is very important in engineering practical value for predicting the vibration stress and fatigue life of the battery pack system.
In recent years, related enterprises and universities are dedicated to researching the vibration fatigue safety of different battery pack system component thickness parameters, and domestic and foreign expert students are also developing systematic research on the vibration fatigue safety of the battery pack system, including optimizing the thickness parameters, adopting novel materials, adopting different battery pack module arrangement modes and other methods, but lacking in mechanical response evaluation of the battery pack system under various loads.
Disclosure of Invention
The invention aims to provide a method for optimizing extrusion stress and vibration fatigue life of a battery pack system, which comprises the following steps:
1) Establishing a finite element model of a battery pack system, and setting the thickness of a finite element model part of the battery pack system;
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the finite element model part of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different part thicknesses;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a finite element model part of a battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the characterization model of the extrusion stress and the fatigue life by utilizing a multi-target genetic algorithm to obtain a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) And screening the pareto solution set of the thickness of the battery pack system component by using a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system.
Further, the step of establishing a finite element model of the battery pack system includes:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of a battery module according to the size and the material of the battery module of the battery pack system;
3) And according to the connection relation of all the components of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
Further, the step of establishing the finite element model of the battery module comprises the following steps:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
Further, the component thickness comprises a long bracket thickness, a lifting lug thickness, a bottom shell thickness, a lower supporting beam thickness, an upper connecting bracket thickness, a lower connecting bracket thickness and an upper bracket thickness in a finite element model of the battery pack system.
Further, the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
Further, the step of testing the system fatigue life of the finite element model of the battery pack system under different vibration conditions comprises the following steps:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the stress of the battery pack system; the vibration working condition parameters comprise a power spectrum density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be born by the finite element model of the battery pack system under the thickness of the current part according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) Repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
Further, the fatigue life is characterized by a number of stress cycles N to fatigue fracture;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein sigma is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are the material constants of the battery pack system.
Further, the third-order response surface model is as follows:
wherein beta is 0 、β i 、β ii 、β ij Representing polynomial coefficients, ρ representing the number of variables; y (x) is the output; x is x i 、x j Is input.
Further, when the representation model of the extrusion stress and the fatigue life is optimized by utilizing the multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are optimization targets of the multi-objective genetic algorithm, and the thickness of the finite element model part of the battery pack system is used as constraint conditions of the multi-objective genetic algorithm.
Further, the step of optimizing the characterization model of the compressive stress and fatigue life using a multi-objective genetic algorithm includes:
1) Randomly generating an initial solution set population with a set scale by adopting a real number coding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of the optimization target;
2) Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and constraint violation value;
3) Operating the initial solution set population through three basic genetic steps of selection, crossing and mutation to obtain a child solution set population of the initial solution set population;
4) Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
5) Combining the parent solution set population and the offspring solution set population to obtain a new solution set population, calculating the crowding distance of each solution individual according to the space position of the objective function value corresponding to each solution individual in the new solution set population, and further selecting a set number of solution individuals in the new solution set population according to the quality degree and the crowding distance of each solution individual to generate the new parent solution set population;
6) Repeating the steps 3) to 5) until the set maximum iteration times are reached, and completing optimization of the characterization model of the extrusion stress and the fatigue life.
The invention has the technical effects that the NSGA-II evaluation model established by the invention can better evaluate the extrusion stress and the vibration fatigue life of the battery pack system, screen out the ideal thickness combination of the components of the battery pack system, and can be used for the double-objective optimization of the stress and the fatigue life of the battery pack system during extrusion and vibration, thereby carrying out the design of the battery pack system with high efficiency and low cost. In addition, the double-target optimization method can be used for designing a battery safety early warning system. The method is used for analyzing the influence of various working conditions on the safety of the battery pack system so as to realize the design of the battery pack system which is stable and safe. The invention solves the multi-objective optimization problem of the mechanical response of the battery pack system under the extrusion working condition and the vibration working condition.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a block diagram of a battery pack system;
in the figure, an upper cover 1, a bottom shell 2, an upper connecting bracket 3, a lower supporting beam 4, a long bracket 5, a short bracket 6, an upper bracket 7, lifting lugs 8, longitudinal beams 9 and a module mounting plate 10.
Detailed Description
The present invention is further described below with reference to examples, but it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. Various substitutions and alterations are made according to the ordinary skill and familiar means of the art without departing from the technical spirit of the invention, and all such substitutions and alterations are intended to be included in the scope of the invention.
Example 1:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) Establishing a finite element model of a battery pack system, and setting the thickness of a finite element model part of the battery pack system;
the step of establishing a finite element model of the battery pack system comprises the following steps:
1.1 Building a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
1.2 According to the size and the material of a battery module of the battery pack system, establishing a finite element model of the battery module;
the step of establishing the finite element model of the battery module comprises the following steps:
1.2.1 According to the size parameters of the battery module, establishing a geometric model of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 According to the connection relation of each component of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
The thickness of the component comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting bracket, the thickness of a lower connecting bracket and the thickness of an upper bracket in a finite element model of the battery pack system.
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
The step of testing the system fatigue life of the finite element model of the battery pack system under different vibration conditions comprises the following steps:
3.1 Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the stress of the battery pack system; the vibration working condition parameters comprise a power spectrum density curve, vibration frequency and amplitude;
3.2 Determining the maximum stress amplitude level which can be born by the finite element model of the battery pack system under the thickness of the current part according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3.3 Repeating steps 3.1) to 3.2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
The fatigue life is characterized by the number of stress cycles N when fatigue fracture is reached;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein sigma is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are the material constants of the battery pack system.
4) Modifying the thickness of the finite element model part of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different part thicknesses;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a finite element model part of a battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system to obtain a representation model of the extrusion stress and the fatigue life;
the third order response surface model is as follows:
wherein beta is 0 、β i 、β ii 、β ij Representing polynomial coefficients, ρ representing the number of variables; y (x) is the output; x is x i 、x j Is input.
6) Optimizing the characterization model of the extrusion stress and the fatigue life by utilizing a multi-target genetic algorithm to obtain a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
when the representation model of the extrusion stress and the fatigue life is optimized by utilizing the multi-target genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are the optimization targets of the multi-target genetic algorithm, and the thickness of the finite element model part of the battery pack system is the constraint condition of the multi-target genetic algorithm.
The step of optimizing the characterization model of the extrusion stress and the fatigue life by utilizing the multi-objective genetic algorithm comprises the following steps:
6.1 Randomly generating an initial solution set population with a set scale by adopting a real number coding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of the optimization target;
6.2 Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and constraint violation value;
6.3 Operating the initial solution set population through three basic genetic steps of selection, crossing and mutation to obtain a child solution set population of the initial solution set population;
6.4 Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
6.5 Combining the parent solution set population and the offspring solution set population to obtain a new solution set population, calculating the crowding distance of each solution individual according to the space position of the objective function value corresponding to each solution individual in the new solution set population, and further selecting a set number of solution individuals in the new solution set population according to the quality degree and the crowding distance of each solution individual to generate the new parent solution set population;
6.6 Repeating the steps 6.3) to 6.5) until the set maximum iteration number is reached, and completing the optimization of the characterization model of the extrusion stress and the fatigue life.
7) And screening the pareto solution set of the thickness of the battery pack system component by using a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system.
Example 2:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) Establishing a finite element model of a battery pack system, and setting the thickness of a finite element model part of the battery pack system;
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the finite element model part of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different part thicknesses;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a finite element model part of a battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the characterization model of the extrusion stress and the fatigue life by utilizing a multi-target genetic algorithm to obtain a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) And screening the pareto solution set of the thickness of the battery pack system component by using a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system.
Example 3:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the step of establishing the finite element model of the battery pack system comprises the following steps:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of a battery module according to the size and the material of the battery module of the battery pack system;
3) And according to the connection relation of all the components of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
Example 4:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 3, wherein the step of establishing the finite element model of the battery module comprises the following steps:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
Example 5:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the thickness of the part comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting bracket and the thickness of a lower connecting bracket in a finite element model of the battery pack system.
Example 6:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the vibration working conditions comprise a random vibration working condition, a positive sweep vibration working condition and a fixed frequency vibration working condition.
Example 7:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the step of testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions comprises the following steps:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the stress of the battery pack system; the vibration working condition parameters comprise a power spectrum density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be born by the finite element model of the battery pack system under the thickness of the current part according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) Repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
Example 8:
an extrusion stress and vibration fatigue life optimization method of a battery pack system is disclosed in the embodiment 2, wherein the fatigue life is characterized by the number of stress cycles N when fatigue fracture is achieved;
the number of stress cycles N satisfies the following formula:
σ m N＝C (1)
wherein sigma is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are the material constants of the battery pack system.
Example 9:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the third-order response surface model is as follows:
wherein beta is 0 、β i 、β ii 、β ij Representing polynomial coefficients, ρ representing the number of variables; y (x) is the output; x is x i 、x j Is input.
Example 10:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are as shown in the embodiment 2, wherein when the characterization model of the extrusion stress and the fatigue life is optimized by utilizing the multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are the optimization targets of the multi-objective genetic algorithm, and the thickness of the finite element model part of the battery pack system is the constraint condition of the multi-objective genetic algorithm.
Example 11:
the main steps of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system are shown in the embodiment 2, wherein the step of optimizing the characterization model of the extrusion stress and the fatigue life by utilizing the multi-objective genetic algorithm comprises the following steps:
1) Randomly generating an initial solution set population with a set scale by adopting a real number coding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of the optimization target;
2) Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and constraint violation value;
3) Operating the initial solution set population through three basic genetic steps of selection, crossing and mutation to obtain a child solution set population of the initial solution set population;
4) Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
5) Combining the parent solution set population and the offspring solution set population to obtain a new solution set population, calculating the crowding distance of each solution individual according to the space position of the objective function value corresponding to each solution individual in the new solution set population, and further selecting a set number of solution individuals in the new solution set population according to the quality degree and the crowding distance of each solution individual to generate the new parent solution set population;
6) Repeating the steps 3) to 5) until the set maximum iteration times are reached, and completing optimization of the characterization model of the extrusion stress and the fatigue life.
Example 12:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
1) And establishing a finite element model of the battery pack system.
The step of establishing a finite element model of the battery pack system comprises the following steps:
1.1 Building a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
1.2 According to the size and the material of a battery module of the battery pack system, establishing a finite element model of the battery module;
the step of establishing the finite element model of the battery module comprises the following steps:
1.2.1 According to the size parameters of the battery module, establishing a geometric model of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 According to the connection relation of each component of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
2) Setting the thickness of a finite element model part of a battery pack system; the thickness of the component comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting bracket, the thickness of a lower connecting bracket and the thickness of an upper bracket in a finite element model of the battery pack system.
3) Testing the system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
4) The method is used for testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions (the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition), and mainly comprises the following steps: defining different vibration working conditions in finite element software by defining different power spectral density curves or vibration frequency, amplitude and the like, then carrying out finite element analysis, and acquiring the fatigue life of a finite element model of a battery pack system by utilizing a fatigue life analysis module or special fatigue life analysis software of the software;
5) Modifying the thickness of the finite element model part of the battery pack system, and returning to the steps 3) and 4) until the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack system are obtained;
6) Establishing a training data set according to the thickness of a finite element model part of a battery pack system and the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system, and constructing a third-order response surface model to obtain a representation model of the extrusion stress and the fatigue life;
7) And (3) obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by utilizing a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thickness of the battery pack system component.
Example 13:
referring to fig. 1 to 2, a method for optimizing compression stress and vibration fatigue life of a battery pack system includes the steps of:
1) And establishing a finite element model of the battery pack system.
The step of establishing a finite element model of the battery pack system comprises the following steps:
1.1 Building a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system; the battery pack system comprises an upper cover 1, a bottom shell 2, an upper connecting bracket 3, a lower supporting beam 4, a long bracket 5, a short bracket 6, an upper bracket 7, lifting lugs 8, longitudinal beams 9 and a module mounting plate 10.
1.2 According to the size and the material of a battery module of the battery pack system, establishing a finite element model of the battery module;
the step of establishing the finite element model of the battery module comprises the following steps:
1.2.1 According to the size parameters of the battery module, establishing a geometric model of the battery module;
1.2.2 Homogenizing the battery module material;
1.2.3 Defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
1.3 According to the connection relation of each component of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
2) Setting the thickness of a finite element model part of a battery pack system; the thickness of the component comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting bracket, the thickness of a lower connecting bracket and the thickness of an upper bracket in a finite element model of the battery pack system.
3) Testing the system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
4) The method is used for testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions (the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition), and mainly comprises the following steps: defining different vibration working conditions in finite element software by defining different power spectral density curves or vibration frequency, amplitude and the like, then carrying out finite element analysis, and acquiring the fatigue life of a finite element model of a battery pack system by utilizing a fatigue life analysis module or special fatigue life analysis software of the software;
5) Modifying the thickness of the finite element model part of the battery pack system, and returning to the steps 3) and 4) until the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack system are obtained;
6) Establishing a training data set according to the thickness of a finite element model part of a battery pack system and the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system, and constructing a third-order response surface model to obtain a representation model of the extrusion stress and the fatigue life;
7) And (3) obtaining a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system by utilizing a multi-target genetic algorithm (NSGA-II) and screening out a pareto solution set of the thickness of the battery pack system component.
Example 14:
a method for optimizing extrusion stress and vibration fatigue life of a battery pack system comprises the following steps:
s1, establishing a finite element model of a battery pack system;
s2, testing the system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
s3, testing the fatigue life of the finite element model of the battery pack system under different thickness combinations under different vibration working conditions;
s4, modifying the thickness of the finite element model part of the battery pack system until the system extrusion stress and the vibration fatigue life of a plurality of finite element models of the battery pack system are obtained;
s5, constructing a third-order response surface model according to the thickness of the finite element model part of the battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system, and obtaining a representation model of the extrusion stress and the fatigue life;
s6, a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system is obtained by utilizing a multi-target genetic algorithm (NSGA-II), and a pareto solution set of the thickness of the battery pack system component is screened out.
Wherein, the step S1 comprises the following sub-steps:
s11, building a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
s12, establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
and S13, coupling the shell finite element model and the battery module finite element model according to the connection relation of all the components of the battery pack system to obtain the battery pack system finite element model.
The beneficial effect of above-mentioned scheme is: according to the invention, the finite element model of the battery pack system is established through the real structural relation of the battery pack system, and the complete data set is acquired through the finite element model of the battery pack system, so that the acquisition cost of the data set is reduced.
The step S12 includes the following sub-steps:
s121, establishing a geometric model of the battery module according to the size parameters of the battery module;
s122, homogenizing the battery module material;
and S123, defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, and obtaining a finite element model of the battery module.
The thickness type in the step S3 includes: the thickness of the long bracket, the thickness of the lifting lug, the thickness of the bottom shell, the thickness of the lower supporting cross beam, the thickness of the upper and lower connecting brackets and the thickness of the upper bracket.
The step S5 includes the following sub-steps:
s51, constructing a third-order response surface model by utilizing the thickness combination of different components and the extrusion stress of a battery pack system under the combination;
s52, constructing a third-order response surface model by using thickness combinations of different components and the vibration fatigue life of the battery pack system under the thickness combinations;
when the third-order response surface model is built in the steps S51 and S52, thickness combination data of different parts are used as input, and corresponding extrusion stress or vibration fatigue life is used as output.
The beneficial effect of above-mentioned scheme is: the complex mapping relation between the combined data with different thicknesses and the extrusion stress and fatigue life of the system is expressed by constructing a third-order response surface model, and the implementation process is simple.
Example 15:
as shown in fig. 1, a method for optimizing the extrusion stress and the vibration fatigue life of a battery pack system comprises the following steps:
s1, establishing a finite element model of a battery pack system;
in this embodiment, the finite element model may be implemented on different finite element software, such as: LS-DYNA or ABAQUS.
Step S1 comprises the following sub-steps:
s11, building a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
in this embodiment, the specific operation of step S11 is as follows: after obtaining the shell size, shell structure and shell material, defining parameters such as the type, size, thickness and material of the shell model in the finite element software, and establishing the shell finite element model.
S12, establishing a finite element model of the battery module according to the size and the material of the battery module of the battery pack system;
the step S12 includes the following sub-steps:
s121, establishing a geometric model of the battery module according to the size parameters of the battery module;
s122, homogenizing the battery module material;
and S123, defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, and obtaining a finite element model of the battery module.
And S13, coupling the shell finite element model and the battery module finite element model according to the connection relation of all the components of the battery pack system to obtain the battery pack system finite element model.
In the step S13, the coupling is to establish a connection relationship between the shell finite element model and the battery module finite element model, where the connection relationship includes: welding, friction, etc.
S2, testing the system extrusion stress of the finite element model of the battery pack system under different thickness combinations under different extrusion loads;
in this embodiment, step S2 specifically includes: based on the requirements of national standard GB38031-2020, according to actual research and development requirements, 120kN extrusion load is selected, extrusion simulation analysis of a battery pack system is carried out, system extrusion stress data of the battery pack system under the condition of different thickness combinations is obtained, and thickness levels of different components of the battery pack system are shown in Table 1.
S3, testing the system vibration stress and the fatigue life of the finite element model of the battery pack system under different thickness combinations under different vibration working conditions;
in this embodiment, step S3 specifically includes: based on the requirements of national standard GB38031-2020, applying vibration loads in three directions according to actual research and development requirements, carrying out vibration simulation analysis of a battery pack system, and acquiring system vibration stress and fatigue life data of the battery pack system under the condition of different thickness combinations.
TABLE 1 thickness levels of the various components of the battery pack system
S4, modifying the thickness of the finite element model part of the battery pack system until the system extrusion stress and the vibration fatigue life of the finite element models of the battery pack system are obtained, wherein the system extrusion stress and the vibration fatigue life of the battery pack system at the thickness level of different parts of the battery pack system are shown in table 2. The method comprises the steps of carrying out a first treatment on the surface of the
S5, constructing a third-order response surface model according to the thickness of the finite element model part of the battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system, and obtaining a representation model of the extrusion stress and the fatigue life;
s51, constructing a third-order response surface model by utilizing the thickness combination of different components and the extrusion stress of a battery pack system under the combination;
s52, constructing a third-order response surface model by using thickness combinations of different components and the vibration fatigue life of the battery pack system under the thickness combinations;
when the third-order response surface model is built in the steps S51 and S52, thickness combination data of different parts are used as input, and corresponding extrusion stress or vibration fatigue life is used as output.
S6, a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system is obtained by utilizing a multi-target genetic algorithm (NSGA-II), and a pareto solution set of the thickness of the battery pack system component is screened out.
Experimental results:
1. the third-order response surface model is built by using the thickness combination of different components and the extrusion stress of the battery pack system under the combination, and is as follows:
2. the third-order response surface model is built by using the thickness combinations of different components and the vibration fatigue life of the battery pack system under the thickness combinations, and is shown as follows:
3. a dual-objective evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system is obtained by utilizing a multi-objective genetic algorithm (NSGA-II), and the pareto solution sets of the thicknesses of the components of the battery pack system are screened, wherein the screened 35 groups of pareto solution sets are shown in Table 3.
TABLE 2 System compressive stress and vibration fatigue Life at the thickness level of the various components of the Battery pack System
TABLE 3 35 Paretop solution sets for NSGA-II screening
In summary, the present embodiment comprehensively considers the problem of the method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system. The result shows that the established NSGA-II evaluation model can better evaluate the extrusion stress and the vibration fatigue life of the battery pack system, and the ideal thickness combination of the components of the battery pack system is screened out, so that the NSGA-II evaluation model can be used for double-objective optimization of the stress and the fatigue life of the battery pack system during extrusion and vibration, and the battery pack system is designed with high efficiency and low cost. In addition, the double-target optimization method can be used for designing a battery safety early warning system. The method is used for analyzing the influence of various working conditions on the safety of the battery pack system so as to realize the design of the battery pack system which is stable and safe.
Claims (7)
1. The method for optimizing the extrusion stress and the vibration fatigue life of the battery pack system is characterized by comprising the following steps of:
1) Establishing a finite element model of a battery pack system, and setting the thickness of a finite element model part of the battery pack system;
2) Testing the system extrusion stress of the finite element model of the battery pack system under different extrusion loads;
3) Testing the system fatigue life of the finite element model of the battery pack system under different vibration working conditions;
4) Modifying the thickness of the finite element model part of the battery pack system, and repeating the steps 2) to 3) to obtain the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system under different part thicknesses;
5) Building a third-order response surface model, and training the third-order response surface model by utilizing the thickness of a finite element model part of a battery pack system, the system extrusion stress and the vibration fatigue life of the finite element model of the battery pack system to obtain a representation model of the extrusion stress and the fatigue life;
6) Optimizing the characterization model of the extrusion stress and the fatigue life by utilizing a multi-target genetic algorithm to obtain a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
7) Screening a pareto solution set of the thickness of a battery pack system component by using a double-target evaluation model of the extrusion stress and the vibration fatigue life of the battery pack system;
the thickness of the component comprises the thickness of a long bracket, the thickness of a lifting lug, the thickness of a bottom shell, the thickness of a lower supporting beam, the thickness of an upper connecting bracket, the thickness of a lower connecting bracket and the thickness of an upper bracket in a finite element model of the battery pack system;
when the representation model of the extrusion stress and the fatigue life is optimized by utilizing the multi-objective genetic algorithm, the extrusion stress and the vibration fatigue life of the battery pack system are the optimization targets of the multi-objective genetic algorithm, and the thickness of the finite element model part of the battery pack system is the constraint condition of the multi-objective genetic algorithm;
the step of optimizing the characterization model of the extrusion stress and the fatigue life by utilizing the multi-objective genetic algorithm comprises the following steps:
6.1 Randomly generating an initial solution set population with a set scale by adopting a real number coding solution mode; the initial solution set population comprises a plurality of solution individuals, and any solution individual is one solution of the optimization target;
6.2 Calculating the fitness and constraint violation value of any solution individual in the initial solution set population according to the optimization target and the constraint condition, and evaluating the quality degree of each solution individual according to the fitness and constraint violation value;
6.3 Operating the initial solution set population through three basic genetic steps of selection, crossing and mutation to obtain a child solution set population of the initial solution set population;
6.4 Evaluating the quality degree of any solution individual in the offspring solution set population according to the fitness and the constraint violation value;
6.5 Combining the parent solution set population and the offspring solution set population to obtain a new solution set population, calculating the crowding distance of each solution individual according to the space position of the objective function value corresponding to each solution individual in the new solution set population, and further selecting a set number of solution individuals in the new solution set population according to the quality degree and the crowding distance of each solution individual to generate the new parent solution set population;
6.6 Repeating the steps 6.3) to 6.5) until the set maximum iteration number is reached, and completing the optimization of the characterization model of the extrusion stress and the fatigue life.
2. The method of optimizing compression stress and vibration fatigue life of a battery pack system according to claim 1, wherein the step of building a finite element model of the battery pack system comprises:
1) Establishing a shell finite element model according to the shell size, the shell structure and the shell material of the battery pack system;
2) Establishing a finite element model of a battery module according to the size and the material of the battery module of the battery pack system;
3) And according to the connection relation of all the components of the battery pack system, coupling the shell finite element model and the battery module finite element model to obtain the battery pack system finite element model.
3. The method for optimizing compression stress and vibration fatigue life of a battery pack system according to claim 2, wherein the step of building a finite element model of the battery module comprises:
1) Establishing a geometric model of the battery module according to the size parameters of the battery module;
2) Homogenizing the battery module material;
3) And defining material parameters of the geometric model of the battery module according to the material information of the battery module obtained by the homogenization treatment, thereby obtaining a finite element model of the battery module.
4. The method for optimizing the compressive stress and vibration fatigue life of a battery pack system according to claim 1, wherein: the vibration working conditions comprise a random vibration working condition, a positive sweep frequency vibration working condition and a fixed frequency vibration working condition.
5. The method for optimizing compression stress and vibration fatigue life of a battery pack system according to claim 1, wherein the step of testing the system fatigue life of the finite element model of the battery pack system under different vibration conditions comprises:
1) Defining vibration working condition parameters in finite element software, and carrying out finite element analysis to obtain the stress of the battery pack system; the vibration working condition parameters comprise a power spectrum density curve, vibration frequency and amplitude;
2) Determining the maximum stress amplitude level which can be born by the finite element model of the battery pack system under the thickness of the current part according to the stress of the battery pack system, and further calculating the fatigue life of the finite element model of the battery pack system;
3) Repeating the steps 1) to 2), thereby obtaining the system fatigue life of the finite element model of the battery pack system under different vibration working conditions.
6. The method of optimizing extrusion stress and vibration fatigue life of a battery pack system according to claim 5, wherein the fatigue life is characterized by a number of stress cycles N to fatigue fracture;
the number of stress cycles N satisfies the following formula:
σ m N＝C(1)
wherein sigma is the maximum stress, and N is the number of stress cycles to achieve fatigue fracture; and m and C are the material constants of the battery pack system.
7. A battery pack according to claim 1The system extrusion stress and vibration fatigue life optimization method is characterized by comprising the following steps of: the third order response surface model is as follows:
wherein beta is 0 、β i 、β ii 、β ij Representing polynomial coefficients, ρ representing the number of variables; y (x) is the output; x is x i 、x j Is input.
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