Artificial Intelligence supported road vehicle suspension design

Doctoral thesis, 2025

This thesis presents an AI-supported framework for vehicle suspension design, combining reinforcement learning (RL) and reverse engineering to automate hardpoint optimization. A case study demonstrates a 50% reduction in design lead time. The proposed framework uses RL to derive suspension kinematics targets from vehicle-level requirements and reverse engineering to convert these targets into hardpoint configurations. The full case study demonstrates the practical application of this integrated methodology. The findings conclude that AI-supported suspension design algorithms significantly enhance both the efficiency and precision of suspension architecture development.

The wheel suspension represents one of the most architecture-intensive systems in automotive design, largely determining a vehicle’s motion characteristics and performance boundaries. Increasing pressures from electrification and intensifying global competition demand accelerated and more efficient development of new vehicle concepts, even within traditional domains like mechanical wheel suspension design. This system encompasses numerous design parameters with intricate interdependencies. Conventionally, development relies heavily on highly specialized engineering expertise. A significant bottleneck in modern suspension development involves balancing complex performance requirements that currently require time-consuming iterations. Today’s development process also involves virtual subjective assessment alongside traditional chassis engineering experience. Addressing these challenges requires a full review of the entire development workflow—from initial target setting through verification and subsequent optimization loops.