A least-squares identification method for vehicle cornering stiffness identification from common vehicle sensor data

Abstract

In this study, we present a robust framework for vehicle tire cornering stiffness identification, leveraging commonly available lateral acceleration and yaw rate measurements. In contrast to most of the state-of-the-art approaches, our methodology does not rely on (augmented) state estimation, but on batch least-squares optimisation of larger time windows. This batch-approach has as a major benefit that many of the observability issues which are encountered in estimation-based methods are completely eliminated. By immediately exploiting the measurement equations, rather than comparing dynamic simulation responses, we obtain a very efficient and sparse formulation which enables online processing. The methodology was validated on (openly available) datasets from two different vehicles – a Ferrari 250LM and Range Rover Evoque, delivering consistent and accurate results for both vehicles. This work shows that batch optimisation can be a very promising alternative for the more common state-estimation approaches for extracting reliable vehicle cornering stiffnesses in various driving scenarios with a much more straightforward tuning.