Abstract
Keywords: Chassis control systems, Design for Six Sigma (DFSS), validation, vehicle variants, Analysis of Variance (ANOVA)
The increasing number of vehicle variants poses a great challenge on the evaluation and validation of chassis control systems with respect to performance. The total number of simulations that would have to be carried out for 100% testing exceeds a range, which can be handled even by using TestAutomation. Hence, a statistically correct approach for reducing the number of necessary simulations is needed. This paper shows the results of an ongoing DFSS project at GME. It shows the simulation environment as well as the statistical evaluation using DoE and analysis of variance (ANOVA) methods. By changing four input parameters (mass, CG height, yaw inertia, axle load distribution) different vehicle configurations are designed. Automated simulations of different driving maneuvers for those vehicles in conjunction with an automated statistical evaluation reveal the interdependencies of the variables of interest. Based on those results the clustering of 26 (real) vehicle variants is shown. The resulting five clusters mean a reduction of about 80% without sacrificing the overall performance.