Development of a Tool for Elasto-Kinematic Suspension Optimization: Application to Double Wishbone and Semi-Virtual Archetypes

The thesis has been prepared in cooperation with Danisi Engineering S.r.l., which is an industry-leading company in the automotive industry, specializing in engineering solutions and prototype development for this sector. The present research proposes a new tool that can optimize elasto-kinematic behavior for suspension systems. State-of-the-art optimization methodologies are reviewed in the present paper, including Evolutionary, Heuristic, Multi-Strategy, and Gradient-Based algorithms. The tool exploits two of the most used algorithms: NSGA-II and MOPSO. Both algorithms have been coupled within the MATLAB-Adams environment in order to ensure easier data exchange. In the methodology, much importance is given to the selection and fine-tuning of these algorithms aiming to produce better performances. Its versatility has already been demonstrated by running it on quite a different suspension archetype-such as Double Wishbone systems with and without pushrod and even a Semi-Virtual configuration-including a company project. Among the Suspension Kinematics and Compliance key parameters of interest to be analyzed in-depth, the toe, camber, spin, kingpin, and caster angles are included. To complement this, an easy-to-use GUI has been developed which has eased the optimization procedure of this tool and has made it quite user-friendly for the engineers operating heavy suspension designs.