To improve the precision, the handling and stability of suspension, a multi-objective optimization method was proposed to overcome the shortcomings of traditional methods which establish the multi-body dynamic model frequently. In this methods，a Response Surface Method (RSM) is proposed as an approximate model for suspension multi-body dynamic model. An improved Non-dominate Sorting Genetic Algorithm (NSGA-Ⅱ) method is used as the optimization algorithm to find the optimal solution. Taking a vehicle front Composite Transverse Leaf Spring McPherson（CTLSM） suspension as an example, the multi-body dynamics suspension model is built based on ADAMS/Car. The parallel travel of CTLSM suspension simulation result shows that the kinematic parameters (Toe angle, Camber angle, Kingpin inclination angle, and Caster angle) are greater than the design target value. To make the kinematic parameters converge to the design value and reduce the variation range, the influence of suspension hard points on them is studied. The coordinates of 36 hard points are selected as design variables. Firstly, the sensitivity analysis is performed using the optimal Latin hypercube design method. The results of the sensitivity analysis determined 8 optimization variables, including lca_outer_x, lca_outer_y, lca_outer_z, strut_top_mount_x, strut_top_mount_y, strut_top_mount_z, tierod_inner_z and tierod_outer_z, which are recorded as x1, x2, x3, x4, x5, x6, x7, x8. The parallel travel simulation results showed that the effects of x3, x7 and x8 on the toe angle are 26.34%, 24.36% and -26.72% respectively. The effect of x3, x5 and x6 on camber angle are -24.48%，36.76% and 22.28% respectively. The effect of x2 and x5 on the Kingpin inclination angle is -32.69% and 49.03% respectively. The effect of x1 and x4 on caster angle is -36.87% and 55.33% respectively. Secondly, the Root Mean Square (RMS) values of the four angles are set as the optimization objectives. The quadratic polynomial model of RSM is used to established the approximation models respectively. To obtain the Pareto optimal solution set, the NSGA-Ⅱ method is applied to optimize the approximate model. The initial value of the toe angle decreased from 0.307° to 0.299°, which was close to the target value 0.283°. The range variation of the toe angle decreased from 0.14°~0.68° to 0.31°~0.49°, where the variation amplitude decreased almost 66.67%. The initial value of the Camber angle increased from 0.3425° to 0.3466°, which was close to the target value 0.35°. The range variation of the camber angle decreased from -0.780°~1.49° to -0.65°~1.38°, where the variation amplitude decreased almost 10.57%. The initial value of the Kingpin inclination angle increased from 12.62° to 13.458°, which was close to the target value 13.417°. The range variation of the Kingpin inclination angle decreased from -11.65°~13.52° to 12.51°~13.44°, where the variation amplitude decreased almost 50.27%. The initial value of the Caster angle decreased from 4.735° to 3.993°, which was close to the target value 4.0°. The range variation of the Caster angle decreased from 4.49°~5.0°to 3.78°~4.22°, where the variation amplitude decreased almost 13.73%. The results show that the proposed method can effectively improve the precision of the model, and lay the foundation for the research of suspension handling and stability performance.

Multi-Objective Optimal Kinematic Design of Composite Transverse Leaf Spring McPherson Suspension

Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2021/volume/85543

ASME 2021 International Mechanical Engineering Congress and Exposition (IMECE2021)

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