This paper proposes a novel unified structure to estimate tire forces. The proposed structure uses estimation modules to calculate/estimate tire forces by means of nonlinear observers. The novelty in the proposed approach lies in the independence of the estimates from the vehicle tire model, thereby making the structure robust against variations in vehicle mass, tire parameters due to tire wear, and, most importantly, road surface conditions. In the proposed structure, we have a dedicated module to estimate the longitudinal tire forces and another to calculate the vertical tire forces. Subsequently, these forces are fed into a third module that utilizes a nonlinear observer to estimate lateral tire forces. The proposed structure is validated through experimental studies

This paper presents a comparative analysis of different analytical methods for identification of vehicle inertial parameters. The effectiveness of four different identification methods namely Recursive Least Squares (RLS), Recursive Kalman Filter (RKF), Gradient, and Extended Kalman Filter (EKF) for estimation of mass, moment of inertia and location of center of gravity of a vehicle is investigated. Requirements, capabilities and drawbacks of each method for real time applications are highlighted based on a comprehensive simulation analysis using CarSim. The Extended Kalman Filter method is shown to be the most reliable method for online identification of vehicle inertial parameters for active vehicle control, vehicle stability, and driver assistant systems.

Accurate information about vehicle longitudinal and lateral velocities is vital for efficient operation of many vehicle control systems. In this paper, an estimation structure to simultaneously estimate vehicle velocities in longitudinal and lateral directions is developed and experimentally validated. This structure includes two parallel estimators: the first estimator is a kinematic-based observer for longitudinal velocity estimation, and the second is a combination of a kinematic-based observer and an inverse tire model to estimate vehicle lateral velocity. The proposed structure can effectively handle the additive biases, which are common in vehicle's stock accelerometers' signals, and provide accurate estimate of vehicle velocities when one (or more than one) wheel experiences the excessive slippage. Additionally, the proposed structure is not sensitive to changes in parameters of tire model and vehicle mass. The performance of this estimation structure is validated by experimental studies. A 0018-9545 (c)

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