Vehicle roll centre estimation with transient dynamics via roll rate

Abstract

The roll centre (RC) is a core parameter for vehicle lateral dynamics and control, which can be obtained via suspension geometry configuration or suspension kinematics and compliance (K&C) test. However, these methodologies are used for laboratory tests and are suitable at low lateral acceleration. In other words, the RC is hard to measure directly while the vehicle is running on the road. In this paper, the online RC estimation methodologies including the adaptive sliding mode observer (ASMO) and the extended Kalman filter (EKF) only with roll rate are proposed considering vehicle transient dynamics. The performance of these algorithms is evaluated and compared with the recursive least square with disturbance observer algorithm (RLSDA) via vehicle dynamics study. Simulation results manifest that, compared with the RLSDA with three roll signals, the proposed ASMO and EKF, only with roll rate, can estimate RC successfully for both the transient and steady-state cases and can be applied for online vehicle RC estimation. In detail, the proposed ASMO is recommended for the steady-state case, and the proposed EKF is recommended for the transient case. Furthermore, the static RC is recommended as the estimation initial value to improve estimation.

KEYWORDS:

• Roll centre estimation
• adaptive sliding mode observer (ASMO)
• extended Kalman filter (EKF)
• vehicle roll dynamics
• vehicle transient dynamics

Acknowledgements

Special thanks are due to the National Natural Science Foundation of China [51675217], the China Automobile Industry Innovation and Development Joint Fund [U1564213] and the Young Elite Scientists Sponsorship Program by CAST [2016QNRC001] for supporting authors’ research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 51675217]; Young Elite Scientists Sponsorship Program by CAST [grant number 2016QNRC001]; China Automobile Industry Innovation and Development Joint Fund [grant number U1564213].