Robust Design Optimization of Vehicle and Adaptive Cruise Control Parameters Considering Fuel Efficiency

Abstract

In the past, the development of an adaptive cruise control (ACC) algorithm considering fuel efficiency and the development of an ACC system considering performances such as fuel efficiency, ride comfort and trackability have been carried out. In addition, research on vehicle and ACC parameters optimization considering fuel efficiency, ride comfort, trackability and safe distance have been carried out. However, in real world, vehicle sprung mass and center of gravity are changed due to the number of vehicle occupants, and there are uncertainties in vehicle parameters such as tire radius, tire spring constant and so on. Therefore, ACC should be designed considering uncertainties due to the variations of vehicle parameters.

In this paper, robust design optimization of vehicle and ACC parameters considering uncertainties is carried out to make the robustness of performances such as fuel efficiency, ride comfort, trackability, and safe distance. Before performing the robust design optimization, vehicle parameters which have significantly influence on the performances are analyzed through analysis of variance (ANOVA) and uncertainty quantification is performed by analyzing information of vehicle occupants. Since numerous function calls of high fidelity model analysis are needed to perform design optimization that kriging surrogate model which is a mathematical model that can replace the high-fidelity model is employed and performed robust design optimization by using constructed kriging surrogate model.