Optimization Design Of Suspension System Of A Distributed Drive Pure Electric Vehicle And Vehicle Performance Analysis

As a separate direction for the development of electric vehicles,the distributed drive pure electric vehicle has the characteristics of low energy consumption and zero emission of traditional electric vehicles.Its unique driving form enables the vehicle to have a series of advantages such as high transmission efficiency,flexible space layout and good dynamic controllability.However,the hub motor increases the unsprung mass of the vehicle and the inertia of the wheel's rotating parts,resulting in reduced ride comfort and increased tire dynamic load.The increase in the dynamic load of the tire weakens the grip of the wheel,thereby reducing the steering stability of the vehicle.In order to solve this problem,based on multi-body dynamics theory and finite element method,this paper establishes a rigidflexible multi-body dynamics model for the design of vehicle.Based on the virtual prototype model,the improved genetic algorithm and proxy model technology are used to optimize the multi-objective optimization of the suspension system based on the suspension performance and vehicle performance.In the process of suspension system performance analysis,in order to evaluate the performance of the suspension,the evaluation method and evaluation index of the suspension K&C characteristics were established,and the reference values of the corresponding indicators were obtained by reference to the vehicle suspension bench test.By designing the simulation analysis and index comparison of the front and rear suspension systems of the vehicle,the design suspension performance is evaluated to find out the insufficiency of the suspension performance.In the optimization process of the suspension system,the corresponding objective function is established for different optimization problems.The sensitivity analysis of the design variables was carried out by parameter research experiment design and orthogonal array test design,and the key design variables of the objective function were screened.Then,based on the selected design variables,the improved optimization algorithm is used to perform multi-objective optimization calculation,and the pareto solution set of the optimization problem is obtained.In the optimization of vehicle performance,the evaluation method and evaluation index of vehicle handling stability and smoothness are established.The reference value of the corresponding index is obtained through real vehicle test,which provides reference for the optimization of vehicle performance.The optimization of vehicle performance is a multivariable,multi-condition and multi-objective optimization problem.The simulation time of the working condition is long,and the number of optimization iterations is many.In order to improve the optimization efficiency,the radial basis neural network proxy model of optimization problem is established and the error analysis is carried out.Based on the established proxy model,the improved genetic algorithm NSGA-II is used to perform indirect optimization calculation,and the pareto solution set of the optimization problem is obtained.According to the vehicle performance optimization goal,the optimal solution is selected,and the correctness of the optimization scheme is verified by comparing the vehicle performance before and after optimization.