Enhanced Gaussian Process Metamodeling and Collaborative Optimization for Vehicle Suspension Design Optimization

Abstract

Dynamic stability is a key performance metric of motor vehicles and has a direct impact on passenger experience and customer satisfaction. The desired vehicle dynamics behavior can be achieved by optimizing the design of vehicle suspensions. Two challenges are associated with this design optimization task. The first one arises from the large number (e.g., 40 or 50) of design variables in modern suspension systems. Such multitude of variables not only makes it expensive to build a training dataset for metamodeling purposes, but also renders accurate surrogate modeling extremely difficult. The second challenge is a lack of guideline for choosing a proper multidisciplinary design optimization (MDO) method for a single MDO problem such as one for vehicle suspension design. In this paper, an enhanced Gaussian process (GP) metamodeling technique is developed and several versions of the collaborative optimization (CO) method are compared via a vehicle suspension design problem. In our enhanced GP modeling method, the model parameters are efficiently estimated using the smoothing effect of the so-called nugget parameter to reduce the search space. In addition, various versions of the CO method are studied where the enhanced collaborative optimization (ECO) method is found to perform the best. A simplified ECO formulation is also investigated to provide insights for future engineering applications.