Research On Geometric Parameters Optimization Of Macpherson Suspension Based On Grey Relational Degree And Improved Entropy Method

The suspension kinematic characteristics have main effects on vehicle handling stability,safety and ride comfort.It is an important index to evaluate suspension performance.The optimization of suspension kinematics characteristics has always been one of the important topics in the automotive industry.The spatial position of suspension hard-points is an important factor affecting the kinematics characteristics of suspension.In the optimization of suspension kinematics characteristics,the design variables which have great influence on the kinematics characteristics are often selected through sensitivity analysis,and the multi-objective optimization function of suspension is developed to gain the best hard-points coordinates so as to optimize the wheel alignment parameters,thereby improve the kinematics characteristics.However,when the design variables are synthetically selected based on sensitivity analysis,the same hard-point coordinate has different influence to each alignment parameter.It is difficult to directly choose the design variables which have a greater impact on the suspension performance,and the design variables are usually selected according to subjective judgment.Moreover,in multi-objective optimization problems,each sub-objective achieves generally the optimal solution with difficulty at the same time,the optimal solution is subjectively selected in the Pareto solution set,and there is no general solution to this issue.And with the number of optimization objectives increasing,the search performance of algorithm decreases sharply,the algorithm scale is enlarged,and the computation process is often time-consuming.To solve the above problems,this paper proposes a optimization strategy based on grey relational degree and improved entropy method,and applies it to geometric parameters optimization of a car's MacPherson suspension.First,an A0 class vehicle with a MacPherson suspension was modeled in ADAMS/Car,and the model reliability was validated against the results of field tests and the vehicle simulation.Second,according to the sensitivity analysis of suspension hard-points coordinates,the grey relational degree and the improved entropy method were used to develop an overall sensitivity evaluation method in order to select the key hard-points coordinates as design variables.Third,for reduce the variation of the front wheel alignment parameters during parallel wheel travel analysis,using fruit fly optimization algorithm(FOA)to train parameters of support vector regression(SVR)to improve the performance of theSVR model,the FOA-SVR approximate model was established between the variation of the front wheel alignment parameters and the suspension hard-points coordinates.Final,a multi-objective optimization function of suspension hard-points was formulated.The weight of each alignment parameter was determined using improved entropy method,the multi-objective optimization function was transformed into a single-objective optimization function by applying the grey relational degree.And the function was optimized using the self-adaptive differential evolution(jDE)algorithm.The results indicated that the variation of the toe angle,camber angle,caster angle and kingpin inclination angle were reduced by 50.21%,31.43%,1.94% and 15.91%,respectively.The proposed geometric parameters optimization strategy of McPherson suspension strategy based on grey relational degree and improved entropy method could effectively improve the kinematics characteristics of the suspension and enhance the vehicle handling stability.