Robustness and Applicability of a Model-Based Tire State Estimator for an Intelligent Tire

Tire states can be estimated by measuring the tire contact patch shape as it varies with vertical load, longitudinal and lateral slip, and so on. In this study, a miniature triaxial accelerometer is used to measure the centripetal accelerations at the tire inner liner. A tire state estimator (TSE) algorithm is developed to transform the measured accelerations to actual tire states, in this case vertical load. The approach used for the TSE is the extended Kalman filter (EKF), but an additional peak detection algorithm is used to synchronize the simulation model with the measurement signal before applying the EKF. The simulation model used in the EKF is an empirical model that describes the basic shape of the centripetal acceleration signal. The applicability of the estimator is assessed by considering the accuracy and robustness for several tire operating conditions: vertical load, velocity, inflation pressure, sideslip, camber, and braking. It is concluded that the TSE exhibits accurate vertical load estimation even in cases of varying load and velocity. Further, it is concluded that the vertical load estimation is robust for (pure) camber changes and (pure) longitudinal force disturbances. For relatively high lateral forces as result of sideslip, the estimation error is larger. The current estimator appears to be not robust for inflation pressure changes, but this can be solved by adding an inflation pressure sensor. Similarly, extension of the estimator to estimate lateral force by adding a second accelerometer not only provides an additional state but also adds the possibility of improving the vertical load estimation. Finally, it is demonstrated that the TSE is able to perform in real time and shows fast convergence capabilities for cases in which the initial vertical load and/or sensor position are unknown or when moving away from situations in which the signal-to-noise ratio is poor.