Estimation of tire forces, road grade, and road bank angle using tire model-less approaches and Fuzzy Logic

This paper presents a modular observer structure to estimate the tire-road forces robustly, avoiding the use of any particular tire model, and using standard signals available in current passenger vehicles. The observer consists of a feedforward longitudinal force estimation block and a hybrid lateral force estimation module formed by an Extended Kalman Filter and a Static Neural Network Structure. Road grade and bank angle are estimated using sensor fusion, where a Fuzzy Logic controller combines the outputs from a Euler Kinematic model and a Recursive Least Squares block. The proposed observer is tested and verified using the simulation software IPG CarMaker® under realistic driving situations. Lastly, the feasibility of the longitudinal force block is proved with real-time experiments.