Efficient modelling and optimization for double wishbone suspensions based on a non-adaptive sampling sparse response surface

ABSTRACTThe response surface method is always used in optimization problems such as the optimization of vehicle suspensions because of its efficiency. A sparse response surface (SRS) is proposed to represent a double wishbone suspension model constructed from a few non-adaptive sampling points. One set of sampling points can be applied to all response surfaces. In this problem, four variations of the front wheel positioning parameters are considered as the optimization objectives, and four parameters are considered as the design variables. Four SRS models with four variables are constructed from 60 sampling points. The interior point method is employed to optimize the problem. The optimization results show that the objective of the camber angle reduces from to , the objective of the caster angle increases from to , the objective of the kingpin inclination angle reduced from to , and the objective of the toe angle increases from to .