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Knowledge of tire side-slip angle and lateral tire force is crucial to vehicle lateral stability control which can enhance vehicle handling and passenger safety. However, due to high costs of sensors and limitations to sensor technologies, ordinary automobiles cannot measure the tire side-slip angle and lateral tire force directly during vehicle operation. Therefore, the accurate, affordable monitoring of lateral vehicle motion is essential. This paper presents a unified observation algorithm to simultaneously estimate the tire side-slip angle and lateral tire force of all four wheels. Firstly, an adaptive-sliding-mode observer (ASMO) is utilized to estimate the lateral tire force of each wheel. Secondly, the tire side-slip angle of each wheel can be estimated by an adaptive compensation algorithm (ACA). Simulations and real car experiments are used to validate the effectiveness of the proposed approach, and extended kalman filter (EKF) is used to compare with our proposed algorithm. Results show that the designed observer can effectively estimate tire side-slip angles and lateral tire forces of all four wheels, and its performance is better than EKF. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm for simultaneously estimating the vehicle mass and road grade for hybrid electric bus (HEB). First, the road grade in current step is estimated using extended Kalman filter (EKF) with the initial state including velocity and engine torque. Second, the vehicle mass is estimated twice, one with EKF and the other with recursive least square (RLS) using the estimated road grade. A more accurate value of the estimated mass is acquired by weighting the trade-off between EKF and RLS. Finally, the road grade and vehicle mass thus obtained are used as the initial states for the next step, and two variables could be decoupled from the nonlinear vehicle dynamics by performing the above procedure repeatedly. Simulation results show that in different starting conditions, the proposed algorithm provides higher accuracy and faster convergence speed, compared with the results using EKF or RLS alone. [ABSTRACT FROM AUTHOR]