Design of a virtual sensing methodology for vehicle ride and comfort applications

Accurate real-time estimation of the instantaneous vehicle state plays a crucial role in modern automotive research, both in the state diagnostics and anomaly detection and in the design and development of advanced control systems and onboard monitoring strategies. In particular, accurate knowledge of chassis motion and wheel dynamics in response to road disturbances is essential for advanced control strategies aimed at simultaneously enhancing ride quality and handling. However, the road profile represents an unmeasured and highly variable input, often requiring complex and costly sensors such as LiDAR for direct observation: this motivates the development of virtual sensing approaches capable of inferring road irregularities from standard onboard sensors. This work presents a novel state observer based on an Extended Kalman Filter (EKF) architecture for the online estimation of road-induced excitations and key vehicle dynamic quantities, including chassis out-of-plane motions, suspension displacements, and tyre-loaded radii. The observer relies on a computationally efficient 7-degree-of-freedom vehicle model, analytically derived through a streamlined multibody formulation, and validated against a high-fidelity multibody reference model under two sensor configurations, both limited to signals typically available in mass-produced vehicles. The results achieved, even when using high-noise measurements, are encouraging for further applications in real-world virtual sensing scenarios.