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. • A virtual sensing methodology is developed to estimate macroscopic road obstacles using acceleration and angular velocity measurements with a reduced-order vehicle model. The logic aims to perform the simultaneous estimation of the out-of-road-plane vehicle and tire motions. • Validation is performed using synthetic data from a 14-dof multibody vehicle model. • The reduced-order model explicitly accounts for suspension constraint effects in tire and chassis dynamics through a simplified formulation: starting from the multibody model as a baseline, nonlinear variations in the dynamic equations—due to instantaneous constraint configurations—are quantitatively analysed over a wide operating domain and approximated using analytical functions. • A nonlinear Kalman filter is designed to optimize estimation accuracy and ensure full state observability. A sensitivity analysis is then carried out for testing the influence of the filter parameters, in particular noise covariance matrix, on the observer performance.