New energy automobile tire constitutive model parameter multi-objective optimization method and system

Abstract

The invention discloses a new energy automobile tire constitutive model parameter multi-objective optimization method and system, relates to the technical field of constitutive model parameter optimization, and is used for solving the technical problems that the optimization precision of tire constitutive model parameters is low and engineering practicability requirements are difficult to meet in the prior art. The new energy automobile tire constitutive model parameter multi-objective optimization method comprises the steps of constructing a multi-objective optimization model of constitutive model parameters of a new energy automobile tire; defining an objective function, constraint conditions and design parameters of the multi-objective optimization model; optimizing lion group initialization and search processes by using a quantum gate, and establishing a storage space and an evaluation space; determining a lion group multi-objective optimization algorithm; and based on the multi-objective optimization model, utilizing a lion group multi-objective optimization algorithm to optimize constitutive model parameters.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of constitutive model parameter optimization, and more particularly, to a new energy vehicle tire constitutive model parameter multi-objective optimization method and system. BACKGROUND

[0002] New energy vehicle tires need to withstand higher instantaneous torque, complex dynamic load and meet strict energy saving requirements, and the precision of tire constitutive model parameters is crucial for new energy vehicle dynamics simulation, durability analysis and handling performance optimization.

[0003] Due to the multi-objective coupling characteristics of new energy vehicle tire material formula and structure design, the traditional single-objective optimization method is difficult to balance the conflict of "simulation accuracy-computing efficiency-parameter robustness", and the increase of target dimension in multi-objective optimization leads to the decline of convergence of traditional algorithm and the lack of diversity of optimal solution, resulting in low finite element simulation accuracy. At the same time, the existing parameter fitting method based on test data has defects such as high trial and error cost and insufficient working condition coverage, and the performance of single optimization algorithm significantly decreases when dealing with high-dimensional constraints. Therefore, the existing technology has low optimization precision of tire constitutive model parameters, which is difficult to meet the engineering practicality requirements. SUMMARY

[0004] The purpose of the present application is to provide a new energy vehicle tire constitutive model parameter multi-objective optimization method and system, which solves the technical problem of low optimization precision of tire constitutive model parameters in the prior art and difficulty in meeting the engineering practicality requirements. In view of this, the present application is realized by the following scheme.

[0005] In a first aspect, the present application provides a new energy vehicle tire constitutive model parameter multi-objective optimization method, comprising: constructing a multi-objective optimization model of the constitutive model parameters of the new energy vehicle tire; defining the objective function, constraint condition and design parameter of the multi-objective optimization model; optimizing lion group initialization and search process using quantum gate, and establishing storage space and evaluation space; determining a lion group multi-objective optimization algorithm; based on the multi-objective optimization model, the lion group multi-objective optimization algorithm is used to optimize the constitutive model parameters.

[0006] Compared with the prior art, in the multi-objective optimization method for constitutive model parameters of new energy vehicle tires of the present invention, after constructing the multi-objective optimization model of the constitutive model parameters of new energy vehicle tires, the objective function, constraints and design parameters of the multi-objective optimization model are defined; further, quantum gates are used to optimize the lion group initialization and search process, and storage space and evaluation space are established; after determining the lion group multi-objective optimization algorithm, the constitutive model parameters are optimized based on the multi-objective optimization model by utilizing the lion group multi-objective optimization algorithm. In the above-mentioned technical solution of the present invention, when constructing a multi-objective optimization model, objective functions, constraints, and design parameters applicable to constitutive models of tires based on various tests and simulations can be determined. The process of defining optimization conditions can be used to ensure that the values ​​obtained from simulations are close to the experimental data, and the robustness of the model can be enhanced. While traditional lion optimization algorithms perform well when handling a small number of objective functions (typically 2-3), as the number of objective functions increases, the complexity of the optimization and the difficulty of solving the objectives increase dramatically due to issues such as the increased dimensionality of the solution space, the difficulty in expressing the Pareto optimal solution set, and the difficulty in expressing the decision maker's preferences and requirements. The present invention utilizes quantum gates to improve the traditional lion optimization algorithm, enhancing its multi-objective optimization capabilities. This technical solution of the present invention provides a high-performance multi-objective optimization algorithm, achieving better convergence of the solution set and superior performance. It also improves the optimization accuracy of tire constitutive model parameters and the accuracy of finite element simulations, resolving the technical problem of low optimization accuracy of tire constitutive model parameters in existing technologies, which makes it difficult to meet engineering practical requirements.

[0007] Furthermore, in the multi-objective optimization method for constitutive model parameters of new energy vehicle tires of the present invention, the process of optimizing the lion group initialization and search using quantum gates includes: A double-chain dynamic coding model is established through the superposition characteristics of quantum bits to achieve parallel evolution of solution vectors in Hilbert space; A phase rotation gate control strategy is adopted to dynamically adjust the quantum state probability amplitude based on the Bloch spherical coordinate transformation, forming a synergistic mechanism between global exploration and local development; The migration pattern of lion social behavior is reconstructed through the quantum tunneling effect, and the screening process of non-dominated solutions is optimized.

[0008] Furthermore, in the multi-objective optimization method for constitutive model parameters of new energy vehicle tires of the present invention, the storage space and evaluation space are storage spaces and evaluation spaces that incorporate an elite retention mechanism.

[0009] Furthermore, in the multi-objective optimization method for constitutive model parameters of new energy vehicle tires of the present invention, the establishment of storage space and evaluation space includes: Set up storage space B t and evaluation space b, and ;in, Indicates storage space. represents the initial value of the j-th quantum bit at the initial moment or any specific stage; The best individual in B0 is stored in the evaluation space b, and starting from B1, the best value of each generation is compared with the previous generation. If the new generation individual is better, it is stored in the storage space B. t middle; Compare storage space B t And the corresponding individual in the evaluation space b, if the storage space B t The best individual in is better and is stored in the evaluation space b, otherwise the evaluation space b remains unchanged; where B0 represents the initial storage space and B1 represents the state after the first layer of quantum circuit operation.

[0010] Furthermore, in the multi-objective optimization method for constitutive model parameters of a new energy vehicle tire of the present invention, in the process of defining the objective function, constraints, and design parameters of the multi-objective optimization model, the design parameters are constitutive model parameters, and the objective function includes minimizing the root mean square error between the strain simulation value and the experimental actual value of the tire at different air pressures; The constraint condition is: when the air pressure and the constitutive model parameters fluctuate on the original constraints, the maximum error threshold generated by the multi-objective optimization model is controlled within a certain range.

[0011] Furthermore, in the multi-objective optimization method for constitutive model parameters of a new energy vehicle tire of the present invention, the root mean square error between the strain simulation value and the experimental actual strain value of the tire at different air pressures is minimized, which is expressed as: ; in, represents the minimum value of the first principal strain root mean square error, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the minimum value of the second root mean square error, It represents the second principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, represents the second principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the Group air pressure, Indicates the measuring points.

[0012] Furthermore, in the multi-objective optimization method for constitutive model parameters of new energy vehicle tires of the present invention, when the quantum gate is used to optimize the lion group initialization and search process, the position of the j-th lion in the t-th generation is expressed as: ; in, represents the position of the jth lion at the tth moment, Represents the angle parameter related to the mth dimension or feature of the jth lion at the tth moment; When the quantum gate is used to optimize the lion group initialization and search process, the quantum gate is used to update the lion group population. The method of using the quantum gate to update the lion group population is expressed as: ; in, It's a quantum revolving door. is the rotation angle; is the qubit probability amplitude vector, and They are the probability amplitudes of the quantum bit before updating; the probability amplitudes are updated through the quantum rotating gate to achieve the update of the lion population.

[0013] Furthermore, in the multi-objective optimization method for constitutive model parameters of a new energy vehicle tire of the present invention, the constitutive model parameters are optimized based on the multi-objective optimization model using the lion group multi-objective optimization algorithm, including: make , initialize the lion group by determining the position of the j-th lion in the t-th generation; Utilize storage space B t And evaluation space b, take the optimal value in B0 and store it in evaluation space b; B t-1 Input to quantum gate Get P t ; By evaluating the population, the algorithm ends when the task requirements are met; in, represents the position of the jth lion in a lion group at time t, j represents the index of a lion in the lion group, π is the circumference of the circle, rand(0,1) represents the random generation of 0 or 1, B0 represents the initial generation lion group information, B t-1 Indicates the The relevant information storage space of the lion group at any time, P t Represents the result obtained after quantum gate operation.

[0014] Furthermore, in the multi-objective optimization method for constitutive model parameters of a new energy vehicle tire of the present invention, the process of determining the first principal strain value and the second principal strain value is: The new energy vehicle tire is inflated at 0.22 MPa, 0.25 MPa, 0.27 MPa and 0.29 MPa respectively, three pieces of strain flowers are used to measure the strain of three measuring points arranged on the tire, so as to determine the first principal strain value and the second principal strain value of the measuring points.

[0015] In the second aspect, the application provides a new energy vehicle tire constitutive model parameter multi-objective optimization system, comprising: A target optimization model construction module is configured to construct a multi-objective optimization model of the constitutive model parameters of the new energy vehicle tire. A model condition definition module is configured to define the objective function, constraint condition and design parameter of the multi-objective optimization model. A lion swarm optimization module is configured to optimize the lion swarm initialization and search process by using quantum gates, and to establish a storage space and an evaluation space. A parameter optimization module is configured to determine a lion swarm multi-objective optimization algorithm, and to optimize the constitutive model parameters by using the lion swarm multi-objective optimization algorithm based on the multi-objective optimization model.

[0016] Compared with the prior art, the new energy vehicle tire constitutive model parameter multi-objective optimization system of the application has the same beneficial effects as the new energy vehicle tire constitutive model parameter multi-objective optimization method described in the above technical solution, which will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0017] The drawings described herein are used to provide further understanding of the application, and form a part of the application. The illustrative embodiments of the application and their descriptions are used to explain the application, and do not constitute an improper limitation on the application. In the drawings: Figure 1 It is a flowchart of the new energy vehicle tire constitutive model parameter multi-objective optimization method of the application; Figure 2 It is a schematic diagram of the tire strain test test points in Example 3 of the application; Figure 3 It is a schematic diagram of the simulation tire LE strain distribution cloud at 0.22 MPa in Example 3 of the application; Figure 4 It is a schematic diagram of the simulation tire LE strain distribution cloud at 0.25 MPa in Example 3 of the application; Figure 5 It is a schematic diagram of the simulation tire LE strain distribution cloud at 0.27 MPa in Example 3 of the application; Figure 6 It is a schematic diagram of the simulation tire LE strain distribution cloud at 0.29 MPa in Example 3 of the application; Figure 7 It is a flowchart of the multi-objective lion swarm optimization algorithm in the application; Figure 8 Schematic diagram of the process of the QMOLSO optimization algorithm in the present invention; Figure 9 Schematic diagram of the PF front of LSO and QMOLSO on DTLZ4 in Example 3; Figure 10 Schematic diagram of the PF front of LSO and QMOLSO on DTLZ5 in Example 3; Figure 11 Schematic diagram of the PF frontier of LSO and QMOLSO on DTLZ6 in Example 3; Figure 12 Schematic diagram of the PF front of LSO and QMOLSO on DTLZ7 in Example 3; Figure 13 Schematic diagram showing the comparison of the first principal strain simulation value errors before and after optimization at 0.22 MPa in Example 3; Figure 14 Schematic diagram showing the comparison of the first principal strain simulation value errors before and after optimization at 0.25 MPa in Example 3; Figure 15 Schematic diagram showing the comparison of the first principal strain simulation value errors before and after optimization at 0.27 MPa in Example 3; Figure 16 Schematic diagram showing the comparison of the first principal strain simulation value errors before and after optimization at 0.29 MPa in Example 3; Figure 17 Schematic diagram showing the comparison of the second principal strain simulation value errors before and after optimization at 0.22 MPa in Example 3; Figure 18 Schematic diagram showing the comparison of the second principal strain simulation value errors before and after optimization at 0.25 MPa in Example 3; Figure 19 Schematic diagram showing the comparison of the second principal strain simulation value errors before and after optimization at 0.27 MPa in Example 3; Figure 20 Schematic diagram comparing the errors of the second principal strain simulation values ​​before and after optimization at 0.29 MPa in Example 3. DETAILED DESCRIPTION

[0018] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] It should be noted that when an element is referred to as being “fixed on” or “disposed on” another element, it may be directly on the other element or indirectly on the other element. When an element is referred to as being “connected to” another element, it may be directly connected to the other element or indirectly connected to the other element.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined. "Several" means one or more, unless otherwise specifically defined.

[0021] Due to the multi-objective coupling of new energy vehicle tire material formulation and structural design, traditional single-objective optimization methods struggle to balance the conflicting requirements of "simulation accuracy, computational efficiency, and parameter robustness." Furthermore, the increased objective dimension in multi-objective optimization leads to decreased convergence of traditional algorithms and insufficient diversity of optimal solutions, resulting in low finite element simulation accuracy. Furthermore, existing parameter fitting methods based on experimental data suffer from high trial-and-error costs and insufficient operating condition coverage, and the performance of a single optimization algorithm significantly degrades when handling high-dimensional constraints. Consequently, existing technologies for optimizing tire constitutive model parameters have low accuracy, making it difficult to meet engineering practicality requirements.

[0022] In order to solve the above technical problems, the present invention provides a multi-objective optimization method for constitutive model parameters of new energy vehicle tires, comprising: Construct a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; defining objective functions, constraints, and design parameters of the multi-objective optimization model; Use quantum gates to optimize the lion group initialization and search process, and establish storage space and evaluation space; Determine a lion group multi-objective optimization algorithm; based on the multi-objective optimization model, optimize the constitutive model parameters using the lion group multi-objective optimization algorithm.

[0023] When adopting the above-mentioned technical solution, in the multi-objective optimization method of the constitutive model parameters of the new energy vehicle tire of the present invention, after constructing the multi-objective optimization model of the constitutive model parameters of the new energy vehicle tire, the objective function, constraints and design parameters of the multi-objective optimization model are defined; further, the quantum gate is used to optimize the lion group initialization and search process, and the storage space and evaluation space are established; after determining the lion group multi-objective optimization algorithm, the constitutive model parameters are optimized based on the multi-objective optimization model by utilizing the lion group multi-objective optimization algorithm. In the above-mentioned technical solution of the present invention, when constructing a multi-objective optimization model, objective functions, constraints, and design parameters applicable to constitutive models of tires based on various tests and simulations can be determined. The process of defining optimization conditions can be used to ensure that the values ​​obtained from simulations are close to the experimental data, and the robustness of the model can be enhanced. While traditional lion optimization algorithms perform well when handling a small number of objective functions (typically 2-3), as the number of objective functions increases, the complexity of the optimization and the difficulty of solving the objectives increase dramatically due to issues such as the increased dimensionality of the solution space, the difficulty in expressing the Pareto optimal solution set, and the difficulty in expressing the decision maker's preferences and requirements. The present invention utilizes quantum gates to improve the traditional lion optimization algorithm, enhancing its multi-objective optimization capabilities. This technical solution of the present invention provides a high-performance multi-objective optimization algorithm, achieving better convergence of the solution set and superior performance. It also improves the optimization accuracy of tire constitutive model parameters and the accuracy of finite element simulations, resolving the technical problem of low optimization accuracy of tire constitutive model parameters in existing technologies, which makes it difficult to meet engineering practical requirements.

[0024] In order to better understand the present invention, the content of the present invention is further explained below in conjunction with specific examples, but the content of the present invention is not limited to the following examples.

[0025] Example 1 See also Figure 1 This embodiment provides a multi-objective optimization method for constitutive model parameters of a new energy vehicle tire, including: Step 1: construct a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; Step 2: define the objective function, constraints and design parameters of the multi-objective optimization model; Step 3: Use quantum gates to optimize the lion group initialization and search process, and establish storage space and evaluation space; Step 4, determine the lion group multi-objective optimization algorithm; based on the multi-objective optimization model, use the lion group multi-objective optimization algorithm to optimize the constitutive model parameters.

[0026] Example 2 This embodiment provides a multi-objective optimization method for constitutive model parameters of a new energy vehicle tire, including: S100, constructing a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; S200, defining the objective function, constraints and design parameters of the multi-objective optimization model; Furthermore, the design parameters are constitutive model parameters, and the objective function includes minimizing the root mean square error between the simulated strain value and the experimental strain value of the tire at different air pressures. The constraint condition is: when the air pressure and the constitutive model parameters fluctuate within the original constraints, the maximum error threshold generated by the multi-objective optimization model is controlled within a certain range; Furthermore, the root mean square error between the strain simulation value and the experimental strain value of the tire at different air pressures is minimized, which can be expressed as: ; in, Indicates the minimum value of the first root mean square error, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the minimum value of the second root mean square error, It represents the second principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, represents the second principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the Group air pressure, Indicates the measuring points; Furthermore, the process of determining the first principal strain value and the second principal strain value is as follows: The tires of new energy vehicles were inflated at tire pressures of 0.22MPa, 0.25MPa, 0.27MPa, and 0.29MPa, respectively. Three strain rosettes were used to measure the strain at three measuring points on the tires to determine the first principal strain value and the second principal strain value of the measuring points.

[0027] S300 uses quantum gates to optimize the lion group initialization and search process, and establishes storage space and evaluation space; Furthermore, the process of optimizing the lion group initialization and search using quantum gates includes: S311, establishes a dual-chain dynamic coding model through the superposition property of quantum bits, and realizes the parallel evolution of solution vectors in Hilbert space; S312 adopts a phase rotation gate control strategy to dynamically adjust the quantum state probability amplitude based on the Bloch spherical coordinate transformation, forming a synergistic mechanism between global exploration and local development; S313, reconstructing the migration pattern of lion social behavior through quantum tunneling effect and optimizing the screening process of non-dominated solutions; Furthermore, the storage space and evaluation space are storage spaces and evaluation spaces integrated with the elite retention mechanism, and the establishment of the storage space and evaluation space includes: S321, set storage space B t and evaluation space b, and ;in, It can be seen as a structure for storing relevant information; during the quantum gate optimization process, it can be used to record the initial quantum bit state configuration, and the subsequent algorithm operations may be adjusted and transformed based on these initial states. (j=1, 2, ..., N) generally represents some initial state of the quantum bit or related binary variables, Indicates the The initial value of a quantum bit at the initial moment or a specific stage, multiple such Constitutes a collection ; S322, store the best individual in B0 in the evaluation space b, and compare the best value of each generation with the previous generation starting from B1. If the new generation individual is better, it will be stored in the storage space B t middle; S323, compared with storage space B t And the corresponding individual in the evaluation space b, if the storage space B t The best individual in is better, and it is stored in the evaluation space b, otherwise the evaluation space b remains unchanged; where B0 represents the initial storage space, that is, all quantum bits are initialized to The state vector, B0=[ , …, ] (a total of m quantum bits) is used as the starting point for algorithm optimization, and then gradually evolves through superposition quantum gate operations; B1 represents the state after the operation of the first layer of quantum circuits, which is generated by the initial storage space B0 through randomly selected quantum gates or trainable gates.

[0028] Furthermore, when the quantum gate is used to optimize the lion group initialization and search process, the position of the j-th lion in the t-th generation is expressed as: ; in, represents the position of the jth lion at the tth moment, Represents the angle parameter related to the mth dimension or feature of the jth lion at the tth moment; When the quantum gate is used to optimize the lion group initialization and search process, the quantum gate is used to update the lion group population. The method of using the quantum gate to update the lion group population is expressed as: ; in, It's a quantum revolving door. is the rotation angle; is the qubit probability amplitude vector, and They are the probability amplitudes of the quantum bit before updating; the probability amplitudes are updated through the quantum rotating gate to achieve the update of the lion population.

[0029] S400, determining a lion group multi-objective optimization algorithm; based on the multi-objective optimization model, optimizing constitutive model parameters using the lion group multi-objective optimization algorithm; Furthermore, the multi-objective optimization model is based on the multi-objective optimization model, and the constitutive model parameters are optimized using the lion group multi-objective optimization algorithm, including: S401, order , initialize the lion group by determining the position of the j-th lion in the t-th generation; S402, using storage space B t And evaluation space b, take the optimal value in B0 and store it in evaluation space b; S403, B t-1 Input to quantum gate Get P t ; S404, the algorithm ends after the population is evaluated and the task requirements are met; in, Indicates the position of the jth lion in a lion group at time t. j represents the index of a lion in the lion group, which is used to distinguish different lion individuals. The value range is generally a positive integer, for example 1, 2, ..., N, where N is the total number of lions in the lion group. π is the circumference of a circle, rand(0, 1) represents the random generation of 0 or 1; B0 represents the initial generation of lion group information, B t-1 Indicates the The relevant information storage space of the lion group at any time can store The position, fitness value and other information of each lion in the lion group at the moment are used to The information of the lion group at this moment is input into the quantum gate to obtain P t , to perform subsequent calculations and update operations, helping the algorithm to continuously search for better solutions; P t It represents the result obtained after the quantum gate operation, which may represent the updated lion group status or position information in the algorithm.

[0030] Example 3 In a first aspect, this embodiment provides a multi-objective optimization method for constitutive model parameters of a new energy vehicle tire, comprising: S100, constructing a multi-objective optimization model for the Yeoh model parameters of new energy vehicle tires; specifically, the construction process of the multi-objective optimization model is as follows: The tire of a new energy vehicle is inflated with a certain air pressure, and the strain at three measuring points is measured using a three-piece strain rosette to obtain the first and second principal strains of the measuring points. The test is repeated at tire pressures of 0.22MPa, 0.25MPa, 0.27MPa, and 0.29MPa. The test point layout is shown in the figure below. Figure 2 As shown; Furthermore, in the finite element processing software Abaqus, the corresponding standard inflation pressure is first applied to the tire according to the test standard, and then the strain data of the tire surface is extracted using the Abaqus visualization post-processing function; the specific steps of the simulation process are: first, according to the content described in the previous section, the main body and tread part of the complex radial tire are established, and it is combined with the rim model into a complete new energy aluminum wheel (with tire) finite element mesh model, and the material constitutive model parameters are assigned to the tire and rim parts respectively. By using the binding function to impose constraints on the tire bead part and the rim contact part, the contact and connection conditions of the corresponding parts of the tire bead and the rim under real conditions are simulated; set the analysis step to constrain the six degrees of freedom of the tire and the rim, and apply a uniform air pressure of 0.25Mpa to the inner surface of the tire to simulate the tire inflation process; repeat the above steps, apply air pressures of 0.22Mpa, 0.27Mpa, and 0.29Mpa to the tire respectively, and obtain the simulation results. The simulation results are as follows Figure 3 、 Figure 4 、 Figure 5 and Figure 6 shown.

[0031] S200, defining the objective function, constraints and design parameters of the multi-objective optimization model; Furthermore, the above objective function, constraints, and design parameters give the model good generalization performance and are applicable to various tire constitutive models based on experiments and simulations. For example, in addition to Yeoh, it is also suitable for multi-objective optimization of model parameters such as Ogden, Neo-Hookean, Vander Waals, and Mooney-Rivlin. Furthermore, the minimum root mean square error of the experimental and simulated strains is used as the optimization objective function for the parameters of the tire constitutive model. Based on actual needs such as ensuring the feasibility of the solution or balancing multi-objective conflicts, different constraints such as robust constraints, interval constraints, or fuzzy constraints are set. For example, robust constraints can make the model stable under parameter perturbations, that is, when the optimization objective function and optimization design parameters fluctuate on the original basis, the maximum error threshold of the model is within a certain range. The main parameters of the tire hyperelastic constitutive model are the value ranges of the optimization design parameters. A multi-objective optimization model for the tire constitutive Yeoh model parameters was established, consisting of the aforementioned objective function, constraints, and design parameters, with the first and second principal strain values ​​at four air pressures and the minimum root mean square error obtained from experiments as the optimization design objectives. Furthermore, the design parameters are constitutive model parameters, and the objective function includes minimizing the root mean square error between the simulated strain value and the experimental strain value of the tire at different air pressures. The constraint condition is: when the air pressure and the constitutive model parameters fluctuate within the original constraints, the maximum error threshold generated by the multi-objective optimization model is controlled within a certain range; Furthermore, the root mean square error between the strain simulation value and the experimental strain value of the tire at different air pressures is minimized, which can be expressed as: (1) in, Indicates the minimum value of the first root mean square error, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the minimum value of the second root mean square error, It represents the second principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, represents the second principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the Group air pressure, Indicates the measuring points; Furthermore, the process of determining the first principal strain value and the second principal strain value is as follows: The tires of new energy vehicles were inflated at pressures of 0.22 MPa, 0.25 MPa, 0.27 MPa, and 0.29 MPa, respectively. Three strain gauge rosettes were used to measure strain at three measuring points on the tires to determine the first and second principal strain values ​​at the measuring points. Furthermore, to obtain ideal strain values ​​and tire constitutive Yeoh model parameters at different air pressures, it is necessary not only to make the values ​​obtained from simulation close to the test data, but also to enhance the robustness of the model. Setting robustness constraints on the model can make the model stable under parameter perturbations. That is, when the air pressure and Yeoh model parameters fluctuate within the original constraints, the maximum error threshold generated by the model is kept within a certain range. The constraint condition function of the model is as follows: (2) in, is the simulation error, is the fluctuation range of inflation pressure and Yeoh model parameters, x represents the principal strain of a certain measuring point, is the maximum allowed error threshold; U is a set containing all possible parameter fluctuation values; Furthermore, for the design parameters, the tire constitutive Yeoh model parameters are used as design variables in the multi-objective optimization model, and are also the parameters that need to be optimally fitted. 10 、C 20 、C 30 and D1 are design variables, and their reasonable value ranges are shown in Table 1.

[0032] Table 1 Optimization parameters of tire constitutive Yeoh model

[0033] S300, see Figure 7 The LSO algorithm is represented by a flowchart. The traditional lion optimization algorithm performs well when processing a small number of objective functions (usually 2-3). However, as the number of objective functions increases, the dimension of the solution space increases, the Pareto optimal solution set, and the decision maker's preferences and needs are difficult to express. These problems lead to a sharp increase in the complexity of the optimization and the difficulty of solving the target. Therefore, this embodiment uses quantum gates to optimize the lion group initialization and search process, and establishes a storage space and an evaluation space. Furthermore, the process of optimizing the lion group initialization and search using quantum gates includes: S311, a dual-chain dynamic coding model is established through the superposition property of quantum bits, which enables the parallel evolution of solution vectors in Hilbert space and significantly expands the dimensionality of the search space; S312 adopts a phase rotation gate control strategy to dynamically adjust the quantum state probability amplitude based on the Bloch spherical coordinate transformation, forming a synergistic mechanism between global exploration and local development; S313, reconstructing the migration pattern of lion social behavior through quantum tunneling effect, and optimizing the screening process of non-dominated solutions by combining Grover quantum search principle; Furthermore, in this embodiment, a quantum optimization algorithm is used to initialize and update the iterative population, establishing a storage space and evaluation space that incorporates an elite retention mechanism. This demonstrates efficient Pareto frontier approximation capabilities in multi-objective combinatorial optimization problems, improving the performance of the multi-objective algorithm. Furthermore, in the above process, quantum coding is mainly used for lion group initialization and lion group update. The moment A lion, its position Expressed as: (3) in, represents the position of the jth lion at the tth moment, Represents the angle parameter related to the mth dimension or feature of the i-th lion at time t; When the quantum gate is used to optimize the lion group initialization and search process, the quantum gate is used to update the lion group population. The method of using the quantum gate to update the lion group population is expressed as: (4) in, It's a quantum revolving door. is the rotation angle; is the qubit probability amplitude vector, and They are the probability amplitudes of the qubits before the update. The probability amplitudes are updated through the quantum rotating gate, thereby updating the lion population.

[0034] The establishment of the storage space and the evaluation space includes: S321, set storage space B t and evaluation space b, and ;in, It can be regarded as a structure for storing relevant information, used to record the initial quantum bit state configuration. , represents the initial value of the j-th quantum bit at the initial moment or any specific stage, for , its form and location Same, and B0=P0; Set up storage space B t and evaluation space b, and ; S322, store the best individual in B0 in the evaluation space b, and compare the best value of each generation with the previous generation starting from B1. If the new generation individual is better, it will be stored in the storage space B t middle; S323, compared with storage space B t And the corresponding individual in the evaluation space b, if the storage space B t The best individual in is better, and it is stored in the evaluation space b, otherwise the evaluation space b remains unchanged; where B0 represents the initial storage space, that is, all quantum bits are initialized to The state vector, B0=[ , …, ] (a total of m quantum bits) is used as the starting point for algorithm optimization, and then gradually evolves through superposition quantum gate operations; B1 represents the state after the operation of the first layer of quantum circuits, which is generated by the initial storage space B0 through randomly selected quantum gates or trainable gates.

[0035] S400, determining a quantum-based multi-objective lion swarm optimization algorithm (Multi-Objective Lion Swarm Optimization algorithm based on Quantum optimization, QMOLSO) by optimizing the lion swarm initialization and search process using the quantum gate; optimizing constitutive model parameters using the lion swarm multi-objective optimization algorithm based on the multi-objective optimization model; Further, see Figure 8 , based on the multi-objective optimization model, optimizing the constitutive model parameters using the lion group multi-objective optimization algorithm includes: S401, order , and then initialize the lion group by determining the position of the j-th lion in the t-th generation according to the above formula (3); represents the position of the j-th lion in a lion group at time t, j represents the index of a lion in the lion group, π represents pi, and rand(0,1) represents the random generation of 0 or 1; S402, using storage space B t and evaluation space b, where , and The form is the same, B0=P0; S403, take the optimal value in B0 and store it in the evaluation space b; S404, B t-1 Input into quantum gate U to get P(t), which is expressed as: (5) in, represents the position vector of a search agent (similar to a lion in a pride) at time t, represents the quantum state or phase parameter of the quantum operation at the position of the j-th lion at time t, represents the quantum state or phase parameter of the quantum operation at the position of the jth lion at time t-1, j represents the position of the jth lion group, Δθi represents the adjustment amount to the phase, B0 represents the initial generation lion group information, B t-1 Indicates the A storage space for relevant information about the lion group at all times.

[0036] S405, evaluate the population, that is, evaluate according to steps S402 and S403 ,If the evaluation space b does not meet the task requirements, go to step S404; S406, comparison and B t-1 The corresponding individual in B t ; Compare with B tand the corresponding individual in b, if B t The best individual in is better, and it is stored in b, otherwise b remains unchanged; S407, evaluation. If the task requirements are not met, go to step S404 and repeat the process. Otherwise, end the algorithm.

[0037] Furthermore, in order to verify the performance of the QMOLSO optimization algorithm in the above-mentioned embodiment 3, DTLZ4, DTLZ5, DTLZ6 and DTLZ7 were selected as test problems to conduct a comprehensive comparative evaluation of the algorithm performance. Specifically, the performance of the multi-objective Loin Swarm Optimization algorithm (LSO) and the QMOLSO optimization algorithm were first compared, and the number of test problem objectives and the test function dimension were set accordingly. The population size of DTLZ4, DTLZ5, DTLZ6 and DTLZ7 was set to 100 and the number of iterations was 500. The PF frontier graphs of LSO and QMOLSO on different test functions are shown in FIG. Figures 9 to 14 The results show that when dealing with multi-objective optimization problems with a large number of iterations, the PF frontier obtained by the QMOLSO optimization algorithm is closer to the true Pareto frontier and has no obvious outliers. This also proves that the improved QMOLSO optimization algorithm has improved performance in dealing with multi-objective optimization problems.

[0038] To further validate the performance of the QMOLSO optimization algorithm, we selected the four multi-objective optimization problem test functions mentioned above and used the IGD value as the evaluation metric to compare it with the NSGA-III, EPRRR, and LSO multi-objective optimization algorithms. The DTLZ test problem set had the number of objectives M set to 3 and 6, the population size set to 100, and the number of iterations set to 500. The comparison results are shown in Table 2 below.

[0039] Table 2 Comparison results of IGD values.

[0040] The results show that in the DTLZ series of tests, the IGD value of the QMOLSO algorithm is the smallest, the convergence of the solution set is better, and the performance is better. It can be considered that the performance of the QMOLSO algorithm is better than other algorithms and can be used for multi-objective optimization.

[0041] Furthermore, the QMOLSO optimization algorithm was used to perform multi-objective optimization on the tire constitutive Yeoh model parameters with 1000 iterations. After the optimization was completed, the optimal design variables were selected from the Pareto front solution set. The optimized tire rubber parameters were written into the finite element model for simulation verification. The error comparison between the simulation and test results before and after optimization is shown in the figure below. Figures 15 to 18 ,as well as Figure 19 and Figure 20The results show that the error between the finite element simulation results and the test results after QMOLSO optimization does not exceed 5%, with the maximum error being only 4.93%, far less than the maximum error of 11.03% before optimization. Comparing the errors of the first and second principal strains at three measuring points at each air pressure before and after optimization, it can be seen that the errors after QMOLSO optimization are smaller than those before optimization. This demonstrates that the improved QMOLSO algorithm effectively optimizes the strain test simulation results and proves the effectiveness of the multi-objective optimization method for the tire constitutive Yeoh model parameters described in Example 3.

[0042] In a second aspect, this embodiment provides a multi-objective optimization system for constitutive model parameters of new energy vehicle tires, including: The target optimization model construction module is used to construct a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; A model condition definition module, used to define the objective function, constraint conditions and design parameters of the multi-objective optimization model; Lion group optimization module, which is used to optimize the lion group initialization and search process using quantum gates, and to establish storage space and evaluation space; The parameter optimization module is used to determine the lion group multi-objective optimization algorithm, and optimize the constitutive model parameters using the lion group multi-objective optimization algorithm based on the multi-objective optimization model.

[0043] In the description of the above embodiments, specific features, structures, materials or characteristics may be combined in an appropriate manner in any one or more embodiments or examples.

[0044] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A multi-objective optimization method for constitutive model parameters of new energy vehicle tires, characterized in that: include: Construct a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; defining objective functions, constraints, and design parameters of the multi-objective optimization model; Use quantum gates to optimize the lion group initialization and search process, and establish storage space and evaluation space; Determine the multi-objective optimization algorithm for Lion Group; Based on the multi-objective optimization model, the constitutive model parameters are optimized using the lion group multi-objective optimization algorithm.

2. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 1, characterized in that: The method of using quantum gates to optimize the lion group initialization and search process includes: A double-chain dynamic coding model is established through the superposition characteristics of quantum bits to achieve parallel evolution of solution vectors in Hilbert space; A phase rotation gate control strategy is adopted to dynamically adjust the quantum state probability amplitude based on the Bloch spherical coordinate transformation, forming a synergistic mechanism between global exploration and local development; The migration pattern of lion social behavior is reconstructed through the quantum tunneling effect, and the screening process of non-dominated solutions is optimized.

3. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 2, characterized in that: The storage space and evaluation space are storage spaces and evaluation spaces integrated with the elite retention mechanism.

4. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 3, characterized in that: The establishment of the storage space and the evaluation space includes: Set up storage space B t and evaluation space b, and ;in, Indicates storage space. represents the initial value of the j-th quantum bit at the initial moment or any specific stage; The best individual in B0 is stored in the evaluation space b, and starting from B1, the best value of each generation is compared with the previous generation. If the new generation individual is better, it is stored in the storage space B. t middle; Compare storage space B t And the corresponding individual in the evaluation space b, if the storage space B t The best individual in is better and is stored in the evaluation space b, otherwise the evaluation space b remains unchanged; where B0 represents the initial storage space and B1 represents the state after the first layer of quantum circuit operation.

5. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 4, characterized in that: In the process of defining the objective function, constraints and design parameters of the multi-objective optimization model, the design parameters are constitutive model parameters, and the objective function includes minimizing the root mean square error between the strain simulation value and the test actual value of the tire at different air pressures; The constraint condition is: when the air pressure and the constitutive model parameters fluctuate on the original constraints, the maximum error threshold generated by the multi-objective optimization model is controlled within a certain range.

6. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 5, characterized in that: The root mean square error between the strain simulation value and the experimental actual strain value of the tire at different air pressures is the smallest, which can be expressed as: ； in, represents the minimum value of the first principal strain root mean square error, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, It represents the first principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the minimum value of the second root mean square error, It represents the second principal strain value of the jth measuring point under the i-th group of air pressure generated by simulation, represents the second principal strain value of the jth measuring point under the i-th group of air pressure obtained from the test, Indicates the Group air pressure, Indicates the measuring points.

7. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 6, characterized in that: When using quantum gates to optimize the lion group initialization and search process, the position of the j-th lion at time t is expressed as: ； in, represents the position of the jth lion at the tth moment, Represents the angle parameter related to the mth dimension or feature of the jth lion at the tth moment; When the quantum gate is used to optimize the lion group initialization and search process, the quantum gate is used to update the lion group population. The method of using the quantum gate to update the lion group population is expressed as: ； in, It's a quantum revolving door. is the rotation angle; is the qubit probability amplitude vector, and are the probability amplitudes of the qubits before updating.

8. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 7, characterized in that: The method of optimizing the constitutive model parameters by using the lion group multi-objective optimization algorithm based on the multi-objective optimization model includes: make , initialize the lion group by determining the position of the jth lion at the tth moment; Utilize storage space B t And evaluation space b, take the optimal value in B0 and store it in evaluation space b; B t-1 Input to quantum gate Get P t ; By evaluating the population, the algorithm ends when the task requirements are met; in, represents the position of the jth lion in a lion group at time t, j represents the index of a lion in the lion group, π is the circumference of the circle, rand(0,1) represents the random generation of 0 or 1, B0 represents the initial generation lion group information, B t-1 Indicates the The relevant information storage space of the lion group at any time, P t Represents the result obtained after quantum gate operation.

9. The multi-objective optimization method for constitutive model parameters of new energy vehicle tires according to claim 8, characterized in that: The process of determining the first principal strain value and the second principal strain value is as follows: The tires of new energy vehicles were inflated at tire pressures of 0.22MPa, 0.25MPa, 0.27MPa, and 0.29MPa, respectively. Three strain rosettes were used to measure the strain at three measuring points on the tires to determine the first principal strain value and the second principal strain value of the measuring points.

10. A multi-objective optimization system for constitutive model parameters of new energy vehicle tires, characterized in that: include: The target optimization model construction module is used to construct a multi-objective optimization model for the constitutive model parameters of new energy vehicle tires; A model condition definition module, used to define the objective function, constraint conditions and design parameters of the multi-objective optimization model; Lion group optimization module, which is used to optimize the lion group initialization and search process using quantum gates, and to establish storage space and evaluation space; The parameter optimization module is used to determine the lion group multi-objective optimization algorithm, and optimize the constitutive model parameters using the lion group multi-objective optimization algorithm based on the multi-objective optimization model.

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