A uniform optimization object function for front wheel orientation parameters of a vehicle is reported, which includes the tolerances of practical values and set values of front wheel orientation parameters under full...A uniform optimization object function for front wheel orientation parameters of a vehicle is reported, which includes the tolerances of practical values and set values of front wheel orientation parameters under full load, and the changing value of each parameter with front wheel fluctuation to build a front suspension model for optimization analysis based on the multi-body dynamic (MD) theory. The original suspension is optimized with this model, and the variation law of each parameter with front wheel fluctuation is obtained. The results of a case study demonstrate that the front wheel orientation parameters of the optimized vehicle are reasonable under typical conditions and the variation of each parameter is in an ideal range with the wheel fluctuating within 40 mm. In addition, the driving performance is improved greatly in the road test and practical use.展开更多
Selecting design variables and determining optimal hard⁃point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension,thereby usually resulting in poor overa...Selecting design variables and determining optimal hard⁃point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension,thereby usually resulting in poor overall suspension kinematic performance.To eliminate the subjectivity of selection,a method transferring multiobjective optimization function into a single⁃objective one through the integrated use of grey relational analysis(GRA)and improved entropy weight method(IEWM)is proposed.First,a comprehensive evaluation index of sensitivities was formulated to facilitate the objective selection of design variables by using GRA,in which IEWM was used to determine the weight of each subindex.Second,approximate models between the variations of the front wheel alignment parameters and the design variables were developed on the basis of support vector regression(SVR)and the fruit fly optimization algorithm(FOA).Subsequently,to eliminate the subjectivity and improve the computational efficiency of multiobjective optimization(MOO)of hard⁃point coordinates,the MOO functions were transformed into a single⁃objective optimization(SOO)function by using the GRA-IEWM method again.Finally,the SOO problem was solved by the self⁃adaptive differential evolution(jDE)algorithm.Simulation results indicate that the GRA⁃IEWM method outperforms the traditional multiobjective optimization method and the original coordinate scheme remarkably in terms of kinematic performance.展开更多
文摘A uniform optimization object function for front wheel orientation parameters of a vehicle is reported, which includes the tolerances of practical values and set values of front wheel orientation parameters under full load, and the changing value of each parameter with front wheel fluctuation to build a front suspension model for optimization analysis based on the multi-body dynamic (MD) theory. The original suspension is optimized with this model, and the variation law of each parameter with front wheel fluctuation is obtained. The results of a case study demonstrate that the front wheel orientation parameters of the optimized vehicle are reasonable under typical conditions and the variation of each parameter is in an ideal range with the wheel fluctuating within 40 mm. In addition, the driving performance is improved greatly in the road test and practical use.
基金Sponsored by the National Natural Science Foundation of China(Grant No.71871078).
文摘Selecting design variables and determining optimal hard⁃point coordinates are subjective in the traditional multiobjective optimization of geometric design of vehicle suspension,thereby usually resulting in poor overall suspension kinematic performance.To eliminate the subjectivity of selection,a method transferring multiobjective optimization function into a single⁃objective one through the integrated use of grey relational analysis(GRA)and improved entropy weight method(IEWM)is proposed.First,a comprehensive evaluation index of sensitivities was formulated to facilitate the objective selection of design variables by using GRA,in which IEWM was used to determine the weight of each subindex.Second,approximate models between the variations of the front wheel alignment parameters and the design variables were developed on the basis of support vector regression(SVR)and the fruit fly optimization algorithm(FOA).Subsequently,to eliminate the subjectivity and improve the computational efficiency of multiobjective optimization(MOO)of hard⁃point coordinates,the MOO functions were transformed into a single⁃objective optimization(SOO)function by using the GRA-IEWM method again.Finally,the SOO problem was solved by the self⁃adaptive differential evolution(jDE)algorithm.Simulation results indicate that the GRA⁃IEWM method outperforms the traditional multiobjective optimization method and the original coordinate scheme remarkably in terms of kinematic performance.
