CN-122020832-A - Dynamic performance and bandwidth optimization method for vehicle expandable chassis

CN122020832ACN 122020832 ACN122020832 ACN 122020832ACN-122020832-A

Abstract

The invention discloses a dynamic performance and bandwidth optimization method of an expandable chassis of a vehicle, which comprises the steps of establishing an expandable chassis multi-body dynamic joint simulation model, acquiring dynamic performance data sets under different chassis configurations, establishing an expandable chassis dynamic performance high-precision prediction agent model based on an integrated learning algorithm, and carrying out optimization solution through a multi-objective optimization algorithm so as to obtain an optimized vehicle chassis structure. The invention is based on the extensible chassis multi-body dynamics joint simulation model, and based on the framework characteristics and the actual working condition requirements of the extensible chassis, realizes the efficient modeling of the vehicle chassis, and simultaneously improves the accuracy and generalization capability of dynamic performance prediction through the extensible chassis dynamics high-accuracy prediction proxy model, optimizes the chassis structure through a multi-objective optimization algorithm, improves the overall dynamics performance of the vehicle, improves the optimization efficiency, and can adapt to the diversified optimization requirements of various vehicle model frameworks.

Inventors

HE ZHICHENG
WEI LAI
CHEN XIANGJING
HUANG YUANYI
GAO HUI
GAO YUAN
CHEN YONG
ZHOU ENLIN

Assignees

湖南大学

Dates

Publication Date: 20260512
Application Date: 20251113
Priority Date: 20250526

Claims (10)

1. A dynamic performance and bandwidth optimization method of an expandable chassis of a vehicle is characterized by comprising the following steps of establishing an expandable chassis multi-body dynamic joint simulation model, acquiring dynamic performance data sets under different chassis configurations based on the expandable chassis multi-body dynamic joint simulation model, wherein the dynamic performance data sets comprise a plurality of groups of corresponding design variables and corresponding optimized target performance parameters, the design variables comprise horizontal positions of a shock absorber, a lower control arm and a steering pull rod in the chassis, the optimized target performance parameters comprise motion states of the chassis under various working conditions, establishing an expandable chassis dynamic performance high-precision prediction proxy model based on an integrated learning algorithm based on the dynamic performance data sets, realizing prediction of the design variables corresponding to the optimized target performance parameters for the expandable chassis multi-body dynamic joint simulation model, defining combinations of the design variables as individuals of a population by a multi-target optimization algorithm, guiding the population to a better area based on positions of the better individuals and the optimal individuals in iteration of a former part, or generating random population direction disturbance to the population based on positions of the best individuals and the optimal individuals, generating random population disturbance to the population based on the positions of the best individuals, guiding the random population in a later part based on the position of the better individuals and the optimal individuals, and performing solution to the forward-expansion of the spatial prediction model based on the three-dimensional optimal individual boundary.
2. The method for optimizing the dynamic performance and the bandwidth of the vehicle expandable chassis according to claim 1 is characterized by comprising the steps of establishing an expandable chassis multi-body dynamic joint simulation model, acquiring dynamic performance data sets under different chassis configurations based on the expandable chassis multi-body dynamic joint simulation model, wherein parameters of the expandable chassis multi-body dynamic joint simulation model comprise wheelbases, front wheelbases, rear wheelbases, servicing quality, front and rear axle weights and front and rear rigidity, selecting chassis configurations with different sizes, and simulating four working conditions of linear acceleration, braking, steady-state rotation and snaking to acquire the dynamic performance data sets.
3. The method for optimizing dynamic performance and bandwidth of a vehicle expandable chassis according to claim 1, wherein the dynamic performance data sets under different chassis configurations are acquired based on an expandable chassis multi-body dynamics joint simulation model, design variables of the dynamic performance data sets comprise upper horizontal coordinates of shock absorbers at the front part and the rear part of the chassis, lower control arm outer horizontal coordinates at the front part and the rear part of the chassis and steering tie rod outer horizontal coordinates at the front part and the rear part of the chassis, the optimized target performance parameters comprise pitch angle gradients in linear acceleration and braking processes, maximum lateral acceleration in steady state rotation processes, side inclination angle gradients at 0.2g of lateral acceleration and steering angle gradients, average side inclination angle of steering wheels and average yaw angular velocity in serpentine driving.
4. The method for optimizing the dynamic performance and the bandwidth of the expandable chassis of the vehicle according to claim 1 is characterized by comprising the steps of establishing an expandable chassis dynamic performance high-precision prediction proxy model based on an integrated learning algorithm based on the dynamic performance data set, selecting six learners of a random forest, an extreme random tree, a gradient lifting tree algorithm and XGBoost, lightGBM, catboost as base learners, selecting a linear regression algorithm as a second-layer classifier, training and predicting the base learners based on the dynamic performance data set, obtaining a predicted value, calculating an evaluation index of each base learner, screening the base learners with three base learners in front of the rank according to a decision coefficient, taking the predicted value of the base learners with three base learners in front of the rank as input, taking the true value of the dynamic performance data set as output, training by adopting the linear regression algorithm as a meta model, and obtaining the integrated model, namely the expandable chassis dynamic performance high-precision prediction proxy model.
5. The method for optimizing the dynamic performance and the bandwidth of the expandable chassis of the vehicle according to claim 1, wherein the method for optimizing and solving the high-precision predictive proxy model of the dynamic performance of the expandable chassis through a multi-objective optimization algorithm comprises the following steps of initializing a population through generating a chaotic sequence with high spatial uniformity, performing extensive searching in a solution space by the algorithm before the current iteration number reaches half of the total iteration number, performing fine searching in the solution space by the algorithm after the current iteration number reaches half of the total iteration number, and performing iterative solving through a random population.
6. The method for optimizing dynamic performance and bandwidth of a vehicle expandable chassis of claim 5, wherein initializing a population by generating a chaotic sequence with high spatial uniformity comprises generating a chaotic sequence, mapping the generated chaotic sequence into an initialized population to complete initialization of the population, wherein individuals of the population represent combinations of design variables; The generation mode of the chaotic sequence is as follows: ; Wherein, the Representing the j+1th chaotic sequence, Representing a j-th chaotic sequence; mapping the generated chaotic sequence into an initialized population, which is expressed as: ; wherein i represents a population number, N represents the number of the population, j represents a population dimension number, and D represents the dimension of the population; The population is initialized by transforming according to the upper and lower boundaries of the population: ; Wherein, the Representing the i-th population, and the number of the groups, Is the j-th dimensional variable value of the i-th population, And L Representing the maximum and minimum values, respectively, in the j-th dimension of the population.
7. The method of optimizing the dynamic performance and bandwidth of a vehicle expandable chassis according to claim 6, wherein the algorithm performs a broad search in solution space before the current number of iterations reaches half of the total number of iterations, expressed as: If JF<0.5: ; Else: ; ; wherein JF represents a random number between 0 and 1, Representing the current position of the population, r, And Random numbers respectively representing 0 to 1; mean value representing fitness value top 50% population location information; Representing a set of random numbers based on brownian motion; Representing an optimal individual; representing the worst individual; and T represents the current iteration number, and T represents the maximum iteration number.
8. The method of optimizing the dynamic performance and bandwidth of a vehicle expandable chassis according to claim 7, wherein after the current iteration number reaches half of the total iteration number, the algorithm performs a fine search in the solution space, expressed as: If JF<0.5: ; Else: ; Wherein, the , And Representing the best, sub-best and third best individuals respectively, Representing a set of random numbers based on the lewy motion.
9. The method of optimizing dynamic performance and bandwidth of a vehicle expandable chassis of claim 8, wherein the iterative solution by random population is expressed as: ; Wherein RF represents A random number within the code pattern, And Each representing a random population.
10. An expandable chassis for a vehicle, characterized in that it is optimized by the dynamic performance and bandwidth optimization method according to any one of claims 1 to 9.

Description

Dynamic performance and bandwidth optimization method for vehicle expandable chassis Technical Field The invention belongs to the technical field of vehicle chassis structure optimization design, and particularly relates to a dynamic performance and bandwidth optimization method of a vehicle expandable chassis. Background In the development technology of automobiles, in order to improve development efficiency and reduce production cost, the implementation of automobile model expansion based on platform architecture becomes a mainstream development means. By means of the platform and modularized design concept, the vehicle enterprise can derive various vehicle types on the same platform. In the development of the chassis structure of the vehicle, the optimization of the dynamic performance is important. The method for the series-connected vehicle chassis structure often depends on a parameterized model simulation iteration mode, and has the problems of complex calculation and low development efficiency, and a large amount of calculation resources are required to be consumed, so that the development period is long and the cost is high. Therefore, in the prior art, development and optimization of a vehicle chassis structure are realized through intelligent learning and optimization technology, but because the dimension of an optimized variable is high and a strong nonlinear relation is presented between the optimized variable and the dynamic performance, the ideal optimization effect is difficult to achieve by a traditional machine learning method and a multi-objective optimization algorithm. The traditional machine learning method is difficult to effectively solve the high-dimensional complex problem, and the multi-objective optimization algorithm has difficulty in processing the optimization of the strong nonlinear relation. Meanwhile, the conventional parametric model simulation iteration method is insufficient in generalization capability when facing different vehicle types and working conditions, and is difficult to meet diversified market demands. Disclosure of Invention The invention aims to overcome the defects of lower efficiency and poorer performance of a performance optimization method of a vehicle chassis structure in the prior art, thereby providing a dynamic performance and bandwidth optimization method of a vehicle expandable chassis. A method for optimizing the dynamic performance and bandwidth of an expandable chassis of a vehicle, comprising the method steps of: Establishing an extensible chassis multi-body dynamics joint simulation model, and acquiring dynamic performance data sets under different chassis configurations based on the extensible chassis multi-body dynamics joint simulation model, wherein the dynamic performance data sets comprise a plurality of groups of corresponding design variables and corresponding optimization target performance parameters, the design variables comprise horizontal positions of a shock absorber, a lower control arm and a steering pull rod in a chassis, and the optimization target performance parameters comprise motion states of the chassis under various working conditions; Based on the dynamics performance data set, an extensible chassis dynamics performance high-precision prediction proxy model based on an integrated learning algorithm is established, and prediction of the design variable corresponding to the optimization target performance parameter is realized aiming at the extensible chassis multi-body dynamics joint simulation model; The method comprises the steps of defining a combination of design variables into individuals of a population through a multi-objective optimization algorithm, conducting random control on the population to move to a better area based on the positions of the better individuals and the optimal individuals in the previous iteration, or conducting random direction disturbance on the population based on the positions of the worst individuals and the optimal individuals in the next iteration, conducting random control on the population to move to the positions of the first three excellent individuals based on the positions of the first three excellent individuals in the next iteration, or conducting reference points to conduct the individuals to diffuse to a solution space boundary in the previous iteration, and conducting optimization solving on the expandable chassis dynamics performance high-precision prediction agent model. Further, an extensible chassis multi-body dynamics joint simulation model is established, dynamic performance data sets under different chassis configurations are obtained based on the extensible chassis multi-body dynamics joint simulation model, parameters of the extensible chassis multi-body dynamics joint simulation model comprise wheelbase, front wheelbase, rear wheelbase, preparation quality, front and rear axle weights and front and rear rigidities, chassis configurations with different sizes ar