TY - GEN
T1 - COMBINED ESTIMATION OF VEHICLE BODY STATE AND SPRUNG MASS BASED ON ELECTRONICALLY CONTROLLED AIR SUSPENSION SYSTEM
AU - Xu, Wanqiu
AU - Yu, Liangyao
AU - Li, Yong
AU - Cheng, Yiming
N1 - Publisher Copyright:
Copyright © 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Electronically controlled air suspension
KW - Kalman filter
KW - Recursive least squares
KW - Sprung mass estimation
KW - Vehicle body state estimation
UR - http://www.scopus.com/inward/record.url?scp=85178569433&partnerID=8YFLogxK
U2 - 10.1115/DETC2023-116417
DO - 10.1115/DETC2023-116417
M3 - Conference contribution
AN - SCOPUS:85178569433
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 25th International Conference on Advanced Vehicle Technologies (AVT)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2023
Y2 - 20 August 2023 through 23 August 2023
ER -