Abstract
In this paper, subspace identification method is used for vehicle lateral dynamics modelling, which is based on input and output data and describes the vehicle behaviour more accurately. By standard vehicle road tests, the effects of three traditional subspace methods are validated in vehicle modelling. The results show that the MOESP and N4SID methods are effective, and the CVA models have the highest estimated precision. Furthermore, the CVA models from different tests are evaluated in order to obtain a proper vehicle model. By contrast, the CVA-step model and CVA-DLC model provide better estimations on yaw rate and side-slip angle, respectively. Moreover, a tyre cornering stiffness estimation method based on identified model is proposed, which is derived on condition that the vehicle system poles are constant. The result shows that the estimation of Kf and Kr based on CVA models is more accurate.
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