A longitudinal force estimation based on wheel dynamics and unscented Kalman filter is proposed in this report to address the difficulties in the conventional tire-based approaches. Although it seems that implementation of a tire model in the estimation procedure should result in more accurate results, especially for non-linear regions, complexities in identifying the tire parameters due to the variation of the road and tire conditions leads to inaccurate results for harsh maneuvers on slippery roads. Moreover, the estimation process requires reliable measurements and this necessitates utilizing dynamic models with feasible measurements. Consequently, wheel dynamics is employed to extend the fidelity of the algorithm. For such a model, wheel speeds as reliable and feasible measurements are available. In this strategy, the complex tire-road interaction can be discarded since the wheel speeds are being observed by wheel sensors and the values of the effective torques are provided by motor drives then the longitudinal forces at each individual corner of the vehicle can be estimated independently. Experimental and simulation results confirm the validity of the algorithm in slippery road conditions as well as normal conditions. The newly developed structure has a strong potential to be integrated with other state estimation, such as longitudinal/lateral velocities and lateral forces.

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