Carbon / glass fiber composite material tensile structural battery pack shell and optimization method

Abstract

The present invention discloses a carbon / glass fiber composite traction structure battery pack shell and optimization method. According to the battery pack shell parameters, a parametric finite element model of N groups of carbon / glass fiber composite traction structure battery pack shells is established to obtain the corresponding collision force peak, absorbed energy, mass and intrusion amount of the carbon / glass fiber composite traction structure battery pack shell when it collides; secondly, four proxy models are constructed, and the multi-objective deterministic optimization model of the collision resistance of the carbon / glass fiber composite traction structure battery pack shell is used to obtain the deterministic optimization results of the design variables; if the deterministic optimization results do not meet the preset reliability, the reliability optimization model of the collision resistance of the carbon / glass fiber composite traction structure battery pack shell is used to obtain the uncertainty optimization results of the design variables. The present invention not only gives full play to the performance advantages of different material bodies while achieving the effect of lightweight, but also takes into account the optimization efficiency and global optimality.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle collision safety, and in particular relates to a carbon / glass fiber composite material tensile structure battery pack shell and an optimization method thereof. Background Art

[0002] When the power battery pack is squeezed or impacted from the outside, the internal structure of the battery pack will be deformed, bringing huge risks of short circuit failure and explosion. For electric vehicles with integrated battery packs, the ground clearance of the power battery pack is small, and foreign objects such as road bumps and stones will pose a huge threat to the safety of the bottom structure of the power battery pack, so its bottom safety protection design is even more important. Through a special tensile structure, it can effectively deform itself to absorb the energy generated by the collision, thereby protecting the structural integrity of the battery pack and the safety of the passengers in the car. In addition, the lightweight design of automobiles under the background of energy conservation and environmental protection runs through the entire body structure development process. The lightweight and crash resistance of the power battery pack are a pair of contradictions, so the present invention proposes a carbon / glass fiber composite tensile structure vehicle power battery pack, and through the uncertainty optimization of the carbon / glass fiber composite tensile structure battery pack shell, the crash resistance and lightweight level of the battery pack shell are compromised to the best, and the robustness of the optimization result is guaranteed.

[0003] The current battery pack shell design and optimization have the following two main deficiencies:

[0004] 1. Battery pack shells are mostly made of a single material and a single thickness, which results in insufficient collaborative design space for lightweight and crash resistance. Carbon fiber reinforced resin composite materials have excellent crash resistance and lightweight levels, but their weak ductility and cost hinder large-scale applications;

[0005] 2. In the optimization method, the traditional deterministic optimization does not consider the impact of fluctuations of uncertain factors on the optimization results, resulting in insufficient robustness of the optimized structure. Summary of the invention

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a method for optimizing a battery pack shell of a carbon / glass fiber composite material.

[0007] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0008] A method for optimizing a battery pack shell of a carbon / glass fiber composite material comprises the following steps:

[0009] Step S1, establishing a parametric finite element model of N groups of carbon / glass fiber composite material tensile structure battery pack shells according to the battery pack shell parameters, and obtaining the corresponding collision force peak, absorbed energy, mass and intrusion amount when the carbon / glass fiber composite material tensile structure battery pack shell collides.

[0010] Step S2, using the particle swarm algorithm to optimize the least squares support vector machine proxy model, taking the tensile structure design variables corresponding to the N groups of carbon / glass fiber composite battery pack shells as input, and taking the collision force peak, absorbed energy, mass and intrusion amount corresponding to the tensile structure battery pack shells of the N groups of carbon / glass fiber composite materials as output, constructing the following four particle swarm algorithm-optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models,The relationships between design variables and peak impact force, absorbed energy, mass, and intrusion volume are determined based on four surrogate models.

[0011] Step S3, taking the collision force peak, absorbed energy, and mass as optimization targets and the intrusion amount as a constraint condition, a multi-objective deterministic optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber composite material with an axial expansion structure is established. The multi-objective deterministic optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber composite material with an axial expansion structure is solved to obtain the deterministic optimization results of the design variables.

[0012] Step S4, if the reliability value of the deterministic optimization result exceeds the preset reliability, the deterministic optimization result of the design variable is used as the final optimization result. If the reliability value of the deterministic optimization result does not exceed the preset reliability, the peak collision force, absorbed energy, and mass are used as optimization targets, and the intrusion amount is used as a constraint condition to establish a reliability optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber composite material tensile expansion structure. Solve the reliability optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber composite material tensile expansion structure to obtain the uncertainty optimization result of the design variable, and the uncertainty optimization result of the design variable is used as the final optimization result.

[0013] Preferably, the method for obtaining the deterministic optimization result of the design variables in step S3 is as follows:

[0014] Step S31, taking the collision force peak, absorbed energy, and mass of the carbon / glass fiber composite material tensile structure battery pack shell as optimization targets, the intrusion amount as a constraint condition, and the mixing ratio λ of carbon fiber and glass fiber, the unit cell bottom length a, the hypotenuse length b, the angle c between the bottom and the hypotenuse, the unit cell thickness d, the ply angle θ, and the number of ply n as design variables, a multi-objective deterministic optimization model for the crashworthiness of the carbon / glass fiber composite material tensile structure battery pack shell is established.

[0015] Step S32, based on the established multi-objective deterministic optimization model, the improved Grey Wolf algorithm performs multi-objective deterministic optimization on the design variables of the carbon / glass fiber composite material tensile structure battery pack shell to obtain the first Pareto solution set.

[0016] Step S33: Select a set of optimal compromise solutions from the first Pareto solution set and output them as deterministic optimization results.

[0017] Preferably, the multi-objective deterministic optimization model for the crashworthiness of the traction structure battery pack shell of the carbon / glass fiber composite material is:

[0018]

[0019] Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of plies, S c The amount of intrusion.

[0020] Preferably, the method for obtaining the uncertainty optimization result of the design variables in step S4 is:

[0021] Step S41, taking the design variables of the carbon / glass fiber composite material traction structure battery pack shell as the random variables of the analysis system, the reliability is calculated by importance sampling technology to obtain the reliability analysis results of the deterministic multi-objective optimization of the carbon / glass fiber composite material traction structure battery pack shell. If the reliability value of each constraint function exceeds the preset reliability, the deterministic optimization result of the design variable obtained in step S3 is the final optimization result. Otherwise, continue to step S42.

[0022] Step S42, taking the collision force peak, absorbed energy, and mass of the carbon / glass fiber composite material tensile structure battery pack shell as optimization targets, and taking the intrusion amount of the carbon / glass fiber composite material tensile structure battery pack shell as a constraint condition, a reliability optimization model for the crashworthiness of the carbon / glass fiber composite material tensile structure battery pack shell is established.

[0023] Step S43, based on the established reliability optimization model, the improved Grey Wolf algorithm is used to perform multi-objective uncertainty optimization on the design variables of the carbon / glass fiber composite material tensile structure battery pack shell to obtain a second Pareto solution set.

[0024] Step S44, selecting a set of optimal compromise solutions from the second Pareto solution set and outputting them as uncertainty optimization results.

[0025] Preferably, the reliability optimization model of the crashworthiness of the battery pack shell of the carbon / glass fiber composite material tensile expansion structure is:

[0026]

[0027] Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, y i is the i-th performance index function, i=1,2,3,1,2,3 correspond to P respectively f , 1 / E,m,P r is the reliability, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of ply layers, S c The amount of intrusion.

[0028] Preferably, the method for establishing the parameterized finite element model of the traction structure battery pack shell of N groups of carbon / glass fiber composite materials in step S1 is as follows:

[0029] Step S11, selecting sample points within the feasible domain of continuous variables and discrete variables according to the battery pack shell parameters.

[0030] The continuous variables include the mixing ratio of carbon fiber and glass fiber in the battery pack shell, the bottom length of the unit cell, the length of the hypotenuse, the angle between the bottom and the hypotenuse, and the thickness of the unit cell. The discrete variables are the laying angle and the number of laying layers. N groups of design sample points are uniformly selected within the preset threshold range of the continuous variable parameters, and N is a natural number greater than 0.

[0031] Step S12, establishing a parameterized finite element model of N groups of carbon / glass fiber composite material traction structure battery pack shells according to the selected sample points.

[0032] Step S13, through the mechanical property test of the carbon / glass fiber composite material, combined with the *MAT_ENHANCED_COMPOSITE_DAMAGE material model, fit the material constitutive model and its parameters of the carbon / glass fiber composite material tensile structure battery pack shell.

[0033] Step S14, establishing the connection relationship between each battery cell and the carbon / glass fiber composite material tensile structure battery pack shell according to the preset rigid cylinder collision model, and setting the collision speed of the rigid cylinder colliding with the carbon / glass fiber composite material tensile structure battery pack shell.

[0034] Step S15, constraining all degrees of freedom of the nodes at the upper end of the battery pack, and defining the contact and output between the cylindrical collision model and the battery pack.

[0035] Step S16, calculating the collision force peak, absorbed energy, mass and penetration amount corresponding to the collision of the carbon / glass fiber composite material tensile structure battery pack shell.

[0036] Preferably, the method for determining the relationship between the design variables and the collision force peak value, absorbed energy, mass and penetration amount according to the four proxy models in step S2 is as follows:

[0037] Step S21, using the particle swarm algorithm to optimize the least squares support vector machine proxy model, taking the design variables (λ, a, b, c, d, θ, n) corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber composite materials as input, and taking the collision force peak value P corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber composite materials as input. f , absorbed energy E, mass m and penetration amount S c As output, the following four particle swarm optimization surrogate models of least squares support vector machines are constructed: f Agent model, E agent model, m agent model and S c Proxy model.

[0038] Step S22, respectively calculate the correlation coefficients R of the four proxy model fittings in step S21 2 and root mean square error RMSE.

[0039] Step S23: for each proxy model, calculate its correlation coefficient R 2 , RMSE are compared with the preset first threshold and the preset second threshold respectively. If the correlation coefficients R 2 If the correlation coefficients R of the four proxy models are greater than or equal to the first threshold, and the root mean square error RMSE is less than or equal to the second threshold, step S3 is executed. Otherwise, steps S1 to S22 are executed again until the correlation coefficients R of the four proxy models are greater than or equal to the first threshold, and the root mean square error RMSE is less than or equal to the second threshold. 2 The root mean square error (RMSE) is less than or equal to the preset second threshold.

[0040] Another object of the present invention is to provide a battery pack shell of a carbon / glass fiber composite material, wherein the battery pack shell is a tensile structure battery pack shell, and the mixing ratio of carbon fiber and glass fiber, unit cell bottom length, hypotenuse length, angle between the bottom and the hypotenuse, unit cell thickness, ply angle, and number of ply of the tensile structure battery pack shell are determined by the battery pack shell optimization method of the carbon / glass fiber hybrid composite material.

[0041] Another object of the present invention is to provide a battery pack shell optimization system of carbon / glass fiber composite materials to implement the battery pack shell optimization method of carbon / glass fiber composite materials, including a parameterized finite element model unit, a proxy model unit, a variable deterministic optimization unit, a variable uncertainty optimization unit, and an output unit of the battery pack shell, wherein:

[0042] The parameterized finite element model unit of the battery pack shell is used to establish a parameterized finite element model of N groups of carbon / glass fiber composite material tensile structure battery pack shells according to the battery pack shell parameters, and obtain the corresponding collision force peak value, absorbed energy, mass and intrusion amount when the carbon / glass fiber composite material tensile structure battery pack shell collides.

[0043] The proxy model unit is used to optimize the least squares support vector machine proxy model based on the particle swarm algorithm, taking the design variables corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber composite materials as input, and taking the collision force peak value, absorbed energy, mass and intrusion amount corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber composite materials as output, to construct the following four particle swarm algorithm optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models,The relationships between design variables and peak impact force, absorbed energy, mass, and intrusion volume are determined based on four surrogate models.

[0044] The variable deterministic optimization unit is used to establish a multi-objective deterministic optimization model for the crashworthiness of a battery pack shell made of a carbon / glass fiber composite material with peak collision force, absorbed energy, and mass as optimization targets and with the amount of intrusion as a constraint condition. The multi-objective deterministic optimization model for the crashworthiness of a battery pack shell made of a carbon / glass fiber composite material with a tensile expansion structure is solved to obtain the deterministic optimization results of the design variables.

[0045] The variable uncertainty optimization unit is used to take the deterministic optimization result of the design variable as the final optimization result if the reliability value of the deterministic optimization result exceeds the preset reliability. If the reliability value of the deterministic optimization result does not exceed the preset reliability, a reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber composite material is established with the collision force peak, absorbed energy, and mass as the optimization targets and the intrusion amount as the constraint condition. The reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber composite material is solved to obtain the uncertainty optimization result of the design variable, and the uncertainty optimization result of the design variable is taken as the final optimization result.

[0046] The output unit is used to output the final optimization result.

[0047] Another object of the present invention is to provide an electronic device, comprising: at least one processor, at least one memory and a communication interface. The processor, memory and communication interface communicate with each other. The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the battery pack shell optimization method of the carbon / glass fiber hybrid composite material.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention utilizes a carbon / glass fiber composite material tensile structure battery pack shell to replace the traditional metal material battery pack shell, which fully utilizes the performance advantages of different material bodies while achieving a lightweight effect.

[0050] 2. Optimize the beam material and cross-sectional thickness in different areas of the beam. By performing reliability analysis to calculate the constraint variables and function reliability, the uncertainty optimization is converted into deterministic optimization, taking into account both optimization efficiency and global optimality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a battery pack housing of a carbon / glass fiber composite material and an optimization method thereof provided in an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of the geometric dimensions of the battery pack shell of the carbon / glass fiber hybrid composite material;

[0053] Figure 3 It is a schematic diagram of a low-speed collision of a battery pack shell of a tensile structure of a carbon / glass fiber hybrid composite material provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these examples are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0055] A method for optimizing a battery pack shell of a carbon / glass fiber hybrid composite material, such as Figure 1-3 As shown, the following steps are included:

[0056] Step S1, establishing a parametric finite element model of N groups of carbon / glass fiber hybrid composite material tensile structure battery pack shells according to the battery pack shell parameters, and obtaining the corresponding collision force peak value, absorbed energy, mass and intrusion amount of the carbon / glass fiber hybrid composite material tensile structure battery pack shell when colliding.

[0057] In another embodiment, a method for establishing a parametric finite element model of a battery pack housing of a traction structure of N groups of carbon / glass fiber hybrid composite materials is as follows:

[0058] Step S11, write the optimal Latin hypercube and uniform experimental design method MATLAB program, and select sample points in the feasible domain of continuous variables and discrete variables. The continuous variables include the mixing ratio of carbon fiber and glass fiber in the battery pack shell (λ), the length of the bottom of the unit cell (a), the length of the hypotenuse (b), the angle between the bottom and the hypotenuse (c), and the thickness of the unit cell (d). The discrete variables are the ply angle (θ: 0°, ±45° and 90°), the number of plies (n: 1, 2, 3, 4), and N groups of design sample points are uniformly selected within the preset threshold range of the continuous variable parameters, and N is a natural number greater than 0.

[0059] Step S12, based on the selected design sample points, a parametric finite element model of a battery pack shell of a tensile structure of N groups of carbon / glass fiber hybrid composite materials is established in combination with MATLAB and HYPERMESH software, and the basic size of the grid is 3 mm.

[0060] Step S13, through the mechanical property test of the carbon / glass fiber hybrid composite material, combined with the *MAT_ENHANCED_COMPOSITE_DAMAGE material model, fit the material constitutive model and its parameters of the tensile structure battery pack shell of the carbon / glass fiber hybrid composite material.

[0061] Step S14, import the preset rigid cylinder collision model into HYPERMESH, establish the connection relationship between each battery cell and the carbon / glass fiber hybrid composite material tensile structure battery pack shell through RBE2, and set the collision speed of the rigid cylinder colliding with the carbon / glass fiber hybrid composite material tensile structure battery pack shell.

[0062] Step S15, constraining all degrees of freedom of the nodes at the upper end of the battery pack, and defining the contact and output between the cylindrical collision model and the battery pack.

[0063] Step S16, calculating the peak collision force P corresponding to the collision of the battery pack shell of the carbon / glass fiber hybrid composite material with tensile expansion structure f , absorbed energy E, mass m and penetration amount S c .

[0064] Step S2, using the particle swarm algorithm to optimize the least squares support vector machine proxy model, taking the design variables corresponding to the N groups of carbon / glass fiber hybrid composite battery pack shells as input, and taking the collision force peak, absorbed energy, mass and intrusion amount corresponding to the N groups of carbon / glass fiber hybrid composite battery pack shells as output, constructing the following four particle swarm algorithm-optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models,The relationships between design variables and peak impact force, absorbed energy, mass, and intrusion volume are determined based on four surrogate models.

[0065] In another embodiment, the method for determining the relationship between the design variables and the peak impact force, absorbed energy, mass, and penetration amount according to the four proxy models is as follows:

[0066] Step S21, using a particle swarm optimization-based least squares support vector machine (PSO-LSSVM) proxy model, taking the design variables (λ, a, b, c, d, θ, n) corresponding to the traction structure battery pack shell of N groups of carbon / glass fiber hybrid composite materials as input, and taking the P corresponding to the traction structure battery pack shell of N groups of carbon / glass fiber hybrid composite materials as input. f , absorbed energy E, mass m and penetration amount S c As output, the following four particle swarm optimization surrogate models of least squares support vector machines are constructed: f Agent model, E agent model, m agent model and S c Proxy model.

[0067] Step S22, respectively calculate the correlation coefficients R of the four proxy model fittings in step S21 2 and root mean square error RMSE.

[0068] Step S23: for each proxy model, calculate its correlation coefficient R 2 , the root mean square error RMSE are compared with the preset first threshold and the preset second threshold respectively; if the correlation coefficient R of the four surrogate models 2 If the correlation coefficients R of the four proxy models are greater than or equal to the first threshold value, and the root mean square error RMSE is less than or equal to the second threshold value, step S3 is executed; otherwise, steps S1 to S22 are executed again until the correlation coefficients R of the four proxy models are greater than or equal to the first threshold value, and the root mean square error RMSE is less than or equal to the second threshold value. 2 The root mean square error (RMSE) is less than or equal to the preset second threshold.

[0069] Step S3, taking the collision force peak, absorbed energy, and mass as optimization targets, and taking the intrusion amount as a constraint condition, a multi-objective deterministic optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber hybrid composite material with an axial expansion structure is established. The multi-objective deterministic optimization model for the crashworthiness of the battery pack shell of the carbon / glass fiber hybrid composite material with an axial expansion structure is solved to obtain the deterministic optimization results of the design variables.

[0070] In another embodiment, the following method is used to obtain the deterministic optimization results of the design variables:

[0071] Step S31, using the peak collision force P of the battery pack shell of the carbon / glass fiber hybrid composite material to expand the structure f , the absorbed energy E and mass m are optimized, and the intrusion amount S cA multi-objective deterministic optimization model for the crashworthiness of the tensile structure battery pack shell of carbon / glass fiber hybrid composite materials is established with the mixing ratio of carbon fiber and glass fiber λ, unit cell bottom length a, hypotenuse length b, angle c between the bottom and the hypotenuse, unit cell thickness d, ply angle θ and number of plies n as constraint conditions.

[0072]

[0073] Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of plies, S c The amount of intrusion.

[0074] Step S32, based on the established multi-objective deterministic optimization model, the improved Grey Wolf algorithm performs multi-objective deterministic optimization on the optimization variables (λ, a, b, c, d, θ, n) of the tensile structure battery pack shell of the carbon / glass fiber hybrid composite material to obtain the first Pareto solution set.

[0075] Step 12), select a set of optimal compromise solutions from the first Pareto solution set and output them as deterministic optimization results.

[0076] Step S4, if the reliability value of the deterministic optimization result exceeds the preset reliability, the deterministic optimization result of the design variable is used as the final optimization result. If the reliability value of the deterministic optimization result does not exceed the preset reliability, the collision force peak, absorbed energy, and mass are used as optimization targets, and the intrusion amount is used as a constraint condition to establish a reliability optimization model for the crashworthiness of the battery pack shell of the tensile expansion structure of the carbon / glass fiber hybrid composite material. Solve the reliability optimization model for the crashworthiness of the battery pack shell of the tensile expansion structure of the carbon / glass fiber hybrid composite material to obtain the uncertainty optimization result of the design variable, and the uncertainty optimization result of the design variable is used as the final optimization result.

[0077] In another embodiment, the uncertainty optimization result of the design variables is obtained by the following method:

[0078] Step S41, taking the optimization variables (λ, a, b, c, d, θ, n) of the carbon / glass fiber hybrid composite material traction structure battery pack shell as the random variables of the analysis system, the reliability is calculated by importance sampling technology to obtain the reliability analysis results of the deterministic multi-objective optimization of the carbon / glass fiber hybrid composite material traction structure battery pack shell. If the reliability value of each constraint function exceeds the preset reliability (the preset reliability is 96%), the deterministic optimization result of the design variable obtained in step S3 is the final optimization result. Otherwise, continue to step S42.

[0079] Step S42, using the peak collision force P of the battery pack shell of the carbon / glass fiber hybrid composite material to expand the structure f , the absorbed energy E and mass m are optimized, and the intrusion amount S c As constraints, a reliability optimization model for the crashworthiness of the tensile structure battery pack shell of a carbon / glass fiber hybrid composite material is established, with the mixing ratio of carbon fiber and glass fiber λ, unit cell bottom length a, hypotenuse length b, angle c between the bottom and the hypotenuse, unit cell thickness d, ply angle θ and number of plies n as design variables (optimization variables).

[0080] The reliability optimization model of the crashworthiness of the tensile structure battery pack shell of carbon / glass fiber hybrid composite material is as follows:

[0081]

[0082] Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, y i is the i-th performance index function, i=1,2,3,1,2,3 correspond to P respectively f , 1 / E,m,P r is the reliability, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of ply layers, S c The amount of intrusion.

[0083] Step S43, based on the established reliability optimization model, the improved Grey Wolf algorithm performs multi-objective uncertainty optimization on the optimization variables (λ, a, b, c, d, θ, n) of the tensile structure battery pack shell of the carbon / glass fiber hybrid composite material to obtain the second Pareto solution set.

[0084] Step S44, selecting a set of optimal compromise solutions from the second Pareto solution set and outputting them as uncertainty optimization results.

[0085] In another embodiment, a battery pack shell of a carbon / glass fiber hybrid composite material is provided, wherein the battery pack shell is a tensile structure battery pack shell, and the mixing ratio of carbon fiber and glass fiber, unit cell bottom length, hypotenuse length, angle between the bottom and the hypotenuse, unit cell thickness, ply angle, and number of ply of the tensile structure battery pack shell are determined by the optimization method of the battery pack shell of the carbon / glass fiber hybrid composite material.

[0086] In another embodiment, a battery pack shell optimization system of a carbon / glass fiber hybrid composite material is provided to implement the battery pack shell optimization method of the carbon / glass fiber hybrid composite material, including a parameterized finite element model unit, a proxy model unit, a variable deterministic optimization unit, a variable uncertainty optimization unit, and an output unit of the battery pack shell, wherein:

[0087] The parameterized finite element model unit of the battery pack shell is used to establish a parameterized finite element model of N groups of carbon / glass fiber hybrid composite material tensile structure battery pack shells according to the battery pack shell parameters, and obtain the corresponding collision force peak value, absorbed energy, mass and intrusion amount of the carbon / glass fiber hybrid composite material tensile structure battery pack shell when colliding.

[0088] The proxy model unit is used to optimize the least squares support vector machine proxy model based on the particle swarm algorithm, taking the design variables corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber hybrid composite materials as input, and taking the collision force peak value, absorbed energy, mass and intrusion amount corresponding to the tensile structure battery pack shell of N groups of carbon / glass fiber hybrid composite materials as output, to construct the following four particle swarm algorithm optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models,The relationships between design variables and peak impact force, absorbed energy, mass, and intrusion volume are determined based on four surrogate models.

[0089] The variable deterministic optimization unit is used to establish a multi-objective deterministic optimization model for the crashworthiness of a battery pack shell made of a carbon / glass fiber hybrid composite material with peak collision force, absorbed energy, and mass as optimization targets and the amount of intrusion as a constraint condition. The multi-objective deterministic optimization model for the crashworthiness of a battery pack shell made of a carbon / glass fiber hybrid composite material with a tensile expansion structure is solved to obtain the deterministic optimization results of the design variables.

[0090] The variable uncertainty optimization unit is used to take the deterministic optimization result of the design variable as the final optimization result if the reliability value of the deterministic optimization result exceeds the preset reliability. If the reliability value of the deterministic optimization result does not exceed the preset reliability, a reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber hybrid composite material is established with the collision force peak, absorbed energy, and mass as the optimization targets and the intrusion amount as the constraint condition. The reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber hybrid composite material is solved to obtain the uncertainty optimization result of the design variable, and the uncertainty optimization result of the design variable is used as the final optimization result.

[0091] The output unit is used to output the final optimization result.

[0092] In another embodiment, an electronic device is provided, comprising: at least one processor, at least one memory and a communication interface. The processor, the memory and the communication interface communicate with each other. The memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the battery pack shell optimization method of the carbon / glass fiber hybrid composite material.

[0093] The present invention uses a carbon / glass fiber hybrid composite material to replace the traditional metal battery pack shell, giving full play to the performance advantages of different material bodies while achieving a lightweight effect. By integrating parametric modeling methods, finite element simulation technology, optimal Latin hypercube design methods, and particle swarm algorithm optimization of the least squares support vector machine proxy model, the improved gray wolf algorithm is used to optimize the beam material and cross-sectional thickness in different areas of the beam, taking into account both optimization efficiency and global optimality.

[0094] The present invention integrates parametric modeling methods, finite element simulation technology, optimal Latin hypercube experimental design methods, and particle swarm algorithm optimization least squares support vector machine (PSO-LSSVM) proxy model, and combines importance sampling technology to perform reliability analysis to calculate constraint variables and function reliability, converts uncertainty optimization into deterministic optimization, and uses an improved gray wolf algorithm to optimize the beam material and cross-sectional thickness in different areas of the beam, thereby shortening the development cost and time of the battery pack shell, and achieving rapid collaborative design of its lightweight and low-speed crashworthiness.

[0095] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing a battery pack shell of a carbon / glass fiber composite material with an axial expansion structure, characterized in that: The following steps are involved: Step S1, establishing a parametric finite element model of N groups of carbon / glass fiber composite material traction structure battery pack shells according to battery pack shell parameters, and obtaining the corresponding collision force peak value, absorbed energy, mass and intrusion amount of the carbon / glass fiber composite material traction structure battery pack shell when colliding; Step S2, using the particle swarm algorithm to optimize the least squares support vector machine proxy model, taking the design variables corresponding to the N groups of carbon / glass fiber composite material traction structure battery pack shells as input, and taking the collision force peak, absorbed energy, mass and intrusion amount corresponding to the N groups of carbon / glass fiber composite material traction structure battery pack shells as output, constructing the following four particle swarm algorithm optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models, based on four surrogate models, determine the relationship between design variables and peak impact force, absorbed energy, mass, and intrusion volume; Step S3, establishing a multi-objective deterministic optimization model for the crashworthiness of a battery pack shell of a carbon / glass fiber composite material with a tensile expansion structure, taking the collision force peak, absorbed energy, and mass as optimization targets and taking the intrusion amount as a constraint condition; solving the multi-objective deterministic optimization model for the crashworthiness of a battery pack shell of a carbon / glass fiber composite material with a tensile expansion structure to obtain a deterministic optimization result of the design variables; The method to obtain the deterministic optimization results of the design variables is as follows: Step S31, taking the collision force peak value, absorbed energy, and mass of the carbon / glass fiber composite material tensile structure battery pack shell as optimization targets, taking the intrusion amount as a constraint condition, taking the mixing ratio λ of carbon fiber and glass fiber, the unit cell bottom length a, the hypotenuse length b, the angle c between the bottom and the hypotenuse, the unit cell thickness d, the ply angle θ, and the number of ply n as design variables, a multi-objective deterministic optimization model for the crashworthiness of the carbon / glass fiber composite material tensile structure battery pack shell is established; The multi-objective deterministic optimization model for the crashworthiness of the carbon / glass fiber composite traction structure battery pack shell is: Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of plies, S c is the amount of intrusion; Step S32, according to the established multi-objective deterministic optimization model, the improved Grey Wolf algorithm performs multi-objective deterministic optimization on the design variables of the carbon / glass fiber composite material traction structure battery pack shell to obtain the first Pareto solution set; Step S33, selecting a set of optimal compromise solutions from the first Pareto solution set and outputting them as deterministic optimization results; Step S4, if the reliability value of the deterministic optimization result exceeds the preset reliability, the deterministic optimization result of the design variable is used as the final optimization result; if the reliability value of the deterministic optimization result does not exceed the preset reliability, a reliability optimization model of the crashworthiness of the battery pack shell of the tensile expansion structure of the carbon / glass fiber composite material is established with the collision force peak, absorbed energy, and mass as optimization targets and the intrusion amount as a constraint condition; the reliability optimization model of the crashworthiness of the battery pack shell of the tensile expansion structure of the carbon / glass fiber composite material is solved to obtain the uncertainty optimization result of the design variable, and the uncertainty optimization result of the design variable is used as the final optimization result; Method to obtain the uncertainty optimization results of design variables: Step S41, taking the design variables of the carbon / glass fiber hybrid composite material traction structure battery pack shell as the random variables of the analysis system, performing reliability calculation by importance sampling technology, and obtaining the reliability analysis results of the deterministic multi-objective optimization of the carbon / glass fiber composite material traction structure battery pack shell; if the reliability value of each constraint function exceeds the preset reliability, the deterministic optimization result of the design variable obtained in step S3 is the final optimization result; otherwise, continue to execute step S42; Step S42, taking the collision force peak value, absorbed energy, and mass of the carbon / glass fiber composite material traction structure battery pack shell as optimization targets, and taking the intrusion amount of the carbon / glass fiber composite material traction structure battery pack shell as a constraint condition, establishing a reliability optimization model for the crashworthiness of the carbon / glass fiber composite material traction structure battery pack shell; The reliability optimization model of the crashworthiness of the tensile structure battery pack shell of carbon / glass fiber composite material is: Among them, P f is the peak collision force, E is the absorbed energy, m is the mass, λ is the mixing ratio of carbon fiber and glass fiber, y i is the i-th performance indicator function, , 1, 2, 3 correspond to P f , 1 / E,m,P r is the reliability, a is the length of the bottom of the unit cell, b is the length of the hypotenuse, c is the angle between the bottom and the hypotenuse, d is the thickness of the unit cell, θ is the ply angle, n is the number of ply layers, S c is the amount of intrusion; Step S43, based on the established reliability optimization model, an improved Grey Wolf algorithm is used to perform multi-objective uncertainty optimization on the design variables of the carbon / glass fiber composite material traction structure battery pack shell to obtain a second Pareto solution set; Step S44, selecting a set of optimal compromise solutions from the second Pareto solution set and outputting them as uncertainty optimization results.

2. The method for optimizing a battery pack shell of a carbon / glass fiber composite material according to claim 1, characterized in that: The method for establishing the parameterized finite element model of the N groups of carbon / glass fiber composite material traction structure battery pack shells in step S1 is as follows: Step S11, selecting sample points in the feasible domain of continuous variables and discrete variables according to the battery pack shell parameters; The continuous variables include the mixing ratio of carbon fiber and glass fiber in the battery pack shell, the length of the unit cell bottom, the length of the hypotenuse, the angle between the bottom and the hypotenuse, and the thickness of the unit cell. The discrete variables are the ply angle and the number of plies. N groups of design sample points are uniformly selected within the preset threshold range of the continuous variable parameters, and N is a natural number greater than 0. Step S12, establishing a parameterized finite element model of N groups of carbon / glass fiber composite material traction structure battery pack shells according to the selected sample points; Step S13, fitting the material constitutive model and parameters of the carbon / glass fiber composite material traction structure battery pack shell by combining the *MAT_ENHANCED_COMPOSITE_DAMAGE material model through the mechanical property test of the carbon / glass fiber composite material; Step S14, establishing a connection relationship between each battery cell and the carbon / glass fiber composite material traction structure battery pack shell according to a preset rigid cylinder collision model, and setting a collision speed of the rigid cylinder colliding with the carbon / glass fiber composite material traction structure battery pack shell; Step S15, constraining all degrees of freedom of the nodes at the upper end of the battery pack, and defining the contact and output between the cylindrical collision model and the battery pack; Step S16, calculating the collision force peak, absorbed energy, mass and penetration amount corresponding to the collision of the carbon / glass fiber composite material tensile structure battery pack shell.

3. The method for optimizing the battery pack shell of carbon / glass fiber composite material according to claim 2, characterized in that: The method for determining the relationship between the design variables and the collision force peak value, absorbed energy, mass and penetration amount according to the four proxy models in step S2 is as follows: Step S21, using the particle swarm algorithm to optimize the least squares support vector machine proxy model, taking the design variables corresponding to the N groups of carbon / glass fiber composite battery pack shells as input, and taking the collision force peak value P corresponding to the N groups of carbon / glass fiber composite battery pack shells as input. f , absorbed energy E, mass m and penetration amount S c As output, the following four particle swarm optimization surrogate models of least squares support vector machines are constructed: f Agent model, E agent model, m agent model and S c Proxy model; Step S22, respectively calculate the correlation coefficients R of the four proxy model fittings in step S21 2 and root mean square error RMSE; Step S23: for each proxy model, calculate its correlation coefficient , and the root mean square error RMSE are compared with the preset first threshold and the preset second threshold respectively; If the correlation coefficients of the four proxy models are 2 are both greater than or equal to a preset first threshold, and the root mean square error RMSE is less than or equal to a preset second threshold, executing step S3; Otherwise, re-execute steps S1 to S22 until the correlation coefficients R of the four proxy models are 2 The root mean square error (RMSE) is less than or equal to the preset second threshold.

4. A carbon / glass fiber composite material tensile structure battery pack shell, characterized in that: The battery pack shell is a tensile structure battery pack shell, and the mixing ratio of carbon fiber and glass fiber, unit cell bottom length, hypotenuse length, angle between the bottom and the hypotenuse, unit cell thickness, ply angle, and number of ply of the tensile structure battery pack shell are determined by the battery pack shell optimization method of the carbon / glass fiber hybrid composite material described in any one of claims 1-3.

5. An optimization system for a battery pack shell optimization method using the carbon / glass fiber hybrid composite material according to any one of claims 1 to 3, characterized in that: It includes a parameterized finite element model unit of a battery pack shell, a proxy model unit, a variable deterministic optimization unit, a variable uncertainty optimization unit, and an output unit, wherein: The parameterized finite element model unit of the battery pack shell is used to establish a parameterized finite element model of N groups of carbon / glass fiber composite material tensile structure battery pack shells according to the battery pack shell parameters, and obtain the corresponding collision force peak value, absorbed energy, mass and intrusion amount of the carbon / glass fiber composite material tensile structure battery pack shell when colliding; The proxy model unit is used to optimize the least squares support vector machine proxy model based on the particle swarm algorithm, take the design variables corresponding to the N groups of carbon / glass fiber composite battery pack shells as input, take the collision force peak, absorbed energy, mass and intrusion amount corresponding to the N groups of carbon / glass fiber composite battery pack shells as output, and construct the following four particle swarm algorithm-optimized least squares support vector machine proxy models: P f Agent model, E agent model, m agent model and S c Surrogate models, based on four surrogate models, determine the relationship between design variables and peak impact force, absorbed energy, mass, and intrusion volume; The variable deterministic optimization unit is used to establish a multi-objective deterministic optimization model for the crashworthiness of a battery pack shell of a carbon / glass fiber composite material with a tensile structure, taking the collision force peak, absorbed energy, and mass as optimization targets and the intrusion amount as a constraint condition; the multi-objective deterministic optimization model for the crashworthiness of a battery pack shell of a carbon / glass fiber composite material with a tensile structure is solved to obtain a deterministic optimization result of the design variable; The variable uncertainty optimization unit is used to take the deterministic optimization result of the design variable as the final optimization result if the reliability value of the deterministic optimization result exceeds the preset reliability; if the reliability value of the deterministic optimization result does not exceed the preset reliability, establish a reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber composite material with the collision force peak, the absorbed energy, and the mass as the optimization target and the intrusion amount as the constraint condition; solve the reliability optimization model for the crashworthiness of the battery pack shell of the tensile structure of the carbon / glass fiber composite material to obtain the uncertainty optimization result of the design variable, and take the uncertainty optimization result of the design variable as the final optimization result; The output unit is used to output the final optimization result.

6. An electronic device, characterized in that: include: at least one processor, at least one memory, and a communication interface; The processor, memory and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the battery pack shell optimization method of the carbon / glass fiber hybrid composite material according to any one of claims 1-3.

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