Enhancing Vehicle Architecture Development: A Robust Approach to Predicting Ride and Handling Performance and Optimization through Reliability Analysis

Global automobile manufacturers are increasingly adopting vehicle architecture development systems in the early stages of product development. This strategic move is aimed at rationalizing their product portfolios based on similar specifications and functions, with the overarching goal of simplifying design complexities and enabling the creation of scalable vehicles. Nevertheless, ensuring consistent performance in this dynamic context poses formidable challenges due to the wide range of design possibilities and potential variations at each development stage. This paper introduces an efficient reliability analysis process designed to identify and mitigate the distribution of Ride and Handling (R&H) performance. We employ a range of reliability analysis techniques, including Latin Hypercube Sampling and the enhanced Dimension Reduction (eDR) method, utilizing various types of models such as surrogate models and multi-body dynamics models. This approach is applied to predict R&H performance and facilitate Reliability-based Design Optimization (RBDO), accounting for design range and accumulated tolerance within suspension geometry, all directed toward achieving target performance. By establishing this efficient reliability analysis process, our study not only advances the understanding of R&H performance but also provides a valuable framework for extending this approach to enhance efficiency in other performance areas during the early stages of vehicle development. This extension holds the potential to elevate development efficiency by fostering performance robustness throughout the entire development process