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To study vehicle dynamics, we need to know the forces and moments acting on the vehicle. The most important forces and moments acting on the vehicle are generated at the tire contact patch. A semi-empirical tire model was developed at Advanced Vehicle Dynamics Lab (AVDL) to use for vehicle simulations for steady-state conditions. In this paper, we extended that model to account for transient conditions. We present the basic concept, the development of the tire model, and selective simulation results. The transient tire model is developed by including the effects of the vertical load variations due to the velocity and the acceleration to the tire characteristic parameters. The simulation was performed for the semi-empirical transient tire model in two scenarios. The vehicle driving and braking maneuver was simulated to present the transient longitudinal tire behavior. The vehicle lane changing maneuver also was performed to present the transient lateral tire behavior.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.