Abstract
Vehicle dynamic parameters are of vital importance to establish feasible vehicle models which are used to provide active controls and automated driving control. However, most vehicle dynamics parameters are difficult to obtain directly. In this paper, a new method, which requires only conventional sensors, is proposed to estimate vehicle dynamic parameters. The influence of vehicle dynamic parameters on vehicle dynamics often involves coupling. To solve the problem of coupling, a two-stage estimation method, consisting of multiple-models and the Unscented Kalman Filter, is proposed in this paper. During the first stage, the longitudinal vehicle dynamics model is used. Through vehicle acceleration/deceleration, this model can be used to estimate the distance between the vehicle centroid and vehicle front, the height of vehicle centroid and tire longitudinal stiffness. The estimated parameter can be used in the second stage. During the second stage, a single-track with roll dynamics vehicle model is adopted. By making vehicle continuous steering, this vehicle model can be used to estimate tire cornering stiffness, the vehicle moment of inertia around the yaw axis and the moment of inertia around the longitudinal axis. The simulation results show that the proposed method is effective and vehicle dynamic parameters can be well estimated.
Highlights
Published: 26 May 2021Nowadays, modern road vehicles are using an increasing number of active systems to improve vehicle safety, passenger comfort, vehicle performance and energy efficiency.Advanced Driver Assistance Systems (ADAS), as well as Automated Driving (AD) technologies, are being increasingly implemented in vehicles, aiming for improved driving safety and passenger comfort [1,2]
In order to obtain completed vehicle dynamic parameters (VDPs) at a low cost, we propose a two-stage estimation method consisting of multiple-models and Unscented Kalman Filter to estimate VDPs
The vehicle model used in this paper is a multiple-model approach which is based on a longitudinal vehicle dynamics model and a single-track with roll dynamics vehicle model, which comprises: the motion in the longitudinal direction x, the longitudinal velocity; the motion in the lateral direction y or lateral velocity; the yaw around the vertical axis z, described by the yaw rate and roll with regard to the longitudinal axis x; and the roll rate [13]
Summary
Modern road vehicles are using an increasing number of active systems to improve vehicle safety, passenger comfort, vehicle performance and energy efficiency. An extended Kalman Filter-based estimator adopting a dynamic vehicle model for determining the vehicle’s longitudinal and lateral velocity as well as the yaw rate is proposed in [16]. In [17], a novel approach based on combined H ∞ and extended Kalman Filter (H ∞ -EKF) is used to estimate the center of gravity position of electric vehicles To implement this estimation algorithm, a simplified vehicle dynamics model is applied to the filter formulation. The method uses the well-known simple linear vehicle models for lateral and roll dynamics and assumes the availability of lateral acceleration, the yaw rate, velocity, and steering angle measurements. In order to obtain completed VDPs at a low cost, we propose a two-stage estimation method consisting of multiple-models and Unscented Kalman Filter to estimate VDPs. In the first stage, the vehicle is set to accelerate/decelerate and the longitudinal vehicle model is used.
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