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For performance improvement of modern vehicle dynamic control systems, exact knowledge of the vehicle motion state and the road conditions is essential. Addressing this problem, the development and validation of a state observer for a four-wheel drive electric vehicle is examined in this article. In particular, a reliable and accurate estimation of the tire slip in longitudinal and lateral direction at each wheel is desired. For this, an Unscented Filter is used to estimate the vehicle chassis and wheel dynamics. At the same time, the estimator is able to adapt the tire model according to the current road conditions by using wheel-individual tire-road friction coefficients. In the beginning, details on the used vehicle model as well as the estimator tuning and implementation procedure are given. The validation takes place first in a vehicle simulation environment to point out the performance of the observer especially in severe driving situations, like a sudden change of road friction conditions during a slalom manoeuver. Afterwards, driving tests with an electric four-wheel drive vehicle are carried out to proof the presented observer concept under real-world conditions. All performed driving manoeuvers are described and the test results are presented and evaluated. [ABSTRACT FROM AUTHOR]

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence. [ABSTRACT FROM AUTHOR]