Development of a Suspension Kinematics Design Tool

Suspension hardpoint definition is one of the first steps in racing car development, as small changes in their position may lead to significant differences in kinematic behaviour. Camber gain, toe variation, Ackermann characteristic and roll centre migration strongly depend on how these points are spatially arranged around the car. For this reason, suspension kinematic synthesis is often an iterative and labour-intensive process, where the engineer adjusts the geometry and evaluates its effect on the main performance targets. This thesis presents a Python-based optimization tool for suspension kinematic synthesis. The tool supports and streamlines this process while preserving the use of existing Excel-based engineering models. It interfaces with validated suspension workbooks, imports the initial geometry and target kinematic curves, updates the selected hardpoint coordinates and evaluates the resulting behaviour through the original calculation environment. The optimization is guided by an objective function based on the weighted difference between target and calculated kinematic values, allowing the user to prioritize selected behaviours according to the specific design objective. A key advantage of the proposed methodology is the possibility to optimize only a subset of the Cartesian coordinates defining the hardpoint locations. The remaining coordinates are kept fixed, ensuring that the process respects packaging constraints, modelling assumptions and engineering judgement. To avoid altering the original files, the procedure is performed on automatically created copies of the Excel workbooks, preserving traceability and the integrity of the validated models. The tool was validated on two representative suspension case studies, showing its ability to converge towards the assigned targets and support a more methodical exploration of the design space. The results validate the proposed workflow and show how it can improve the efficiency of suspension development by reducing manual trial-and-error iterations and providing a repeatable framework for kinematic optimization.