Abstract
Aiming at vehicles equipped with the electronically controlled air suspension system, precise body status signals are needed as control inputs during ride comfort and stability control. However, there is an error between the signals measured by height sensors in the system and the actual body height changes due to the sensor installation and measurement principle. Therefore, a combined estimation method based on the Kalman filter and recursive least squares is proposed to solve the above problem, and the vehicle body state and sprung mass are estimated simultaneously. In this paper, a seven-degree-of-freedom dynamics model of the air suspension system is established. Based on this model, a Kalman filter estimator is established to estimate the state of the body, and the recursive least squares method is introduced to estimate the sprung mass of the vehicle to reduce the deviation in the state estimation. Finally, a simulation platform is built and the effectiveness of the proposed method is verified under the condition of the double lane change. The results show that the variation of sprung mass will deteriorate the state estimation results of the Kalman filter estimator, and the combined estimator of the Kalman filter and recursive least squares can effectively improve the accuracy of body state estimation.
