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.

侧倾中心 (RC) 是车辆横向动力学和控制的核心参数，可通过悬架几何配置或悬架运动学和柔顺性 (K&C) 测试获得。然而，这些方法用于实验室测试，适用于低横向加速度。换句话说，当车辆在道路上行驶时，RC 很难直接测量。在本文中，考虑车辆瞬态动力学，提出了包括自适应滑模观测器（ASMO）和仅具有侧倾率的扩展卡尔曼滤波器（EKF）在内的在线RC估计方法。这些算法的性能通过车辆动力学研究与递归最小二乘扰动观测器算法 (RLSDA) 进行了评估和比较。仿真结果表明，与具有三个侧倾信号的 RLSDA 相比，所提出的仅具有侧倾率的 ASMO 和 EKF 可以成功地估计瞬态和稳态情况下的 RC，并可应用于在线车辆 RC 估计。具体来说，建议的 ASMO 建议用于稳态情况，建议的 EKF 建议用于瞬态情况。此外，建议使用静态 RC 作为估计初始值以改进估计。