A Virtual Sensor for Integral Tire Force Estimation using Tire Model-Less Approaches and Adaptive Unscented Kalman Filter

In this paper, a novel approach to estimate the longitudinal, lateral and vertical tire forces is presented. The innovation lies a) in the proposition of a modular state estimation architecture that lessens the tuning effort and ensures the filter’s stability and b) in the estimation of the longitudinal velocity relying only on the wheel speed information.The longitudinal forces are estimated using an Adaptive Random-Walk Linear Kalman Filter. The lateral forces per axle are estimated by combining an Adaptive Unscented Kalman filter and Neural Networks. The individual tire lateral forces are inferred from the axle lateral forces using the vertical load proportionality principle. The individual tire vertical forces are estimated using a steady-state weight transfer approach, in which the roll stiffness distribution is considered. The state estimator is implemented in Simulink R and simulations are carried out in the vehicle dynamics simulation software IPG CarMaker R . The virtual sensor is tested in aggressive and steady-state maneuvers, exhibiting in both cases a remarkable performance.