The distance from the center of gravity (CG) of the sprung mass to the roll axis, referred to as the relative CG height, is a critical parameter in vehicle roll motion. Although the nominal value of the relative CG height can be measured, its actual value generally varies due to different vehicle loading conditions. To facilitate the control of vehicle roll motion, this paper presents a model-based in-vehicle estimation of the relative CG height. The parameter estimation utilizes information measured by common in-vehicle sensors and employs an approach for the parameter estimation in stochastic gray-boxes models. An Extended Kalman Filter (EKF) is developed based on a linear vehicle yaw/lateral/roll model and the best estimate was solved by minimizing the EKF prediction error. A simplified estimation algorithm for in-vehicle implementation is also presented; the simplified algorithm limits the parameter space to a finite number of candidate parameters and the candidate that yields the smallest EKF innovation is identified as the best estimate. The estimation results with vehicle experimental data are included to verify the effectiveness of the proposed design.

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