A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches. (June 2020)

• Record Type: Journal Article
• Authors: Li, Xiaoyu; Xu, Nan; Li, Qin; Guo, Konghui; Zhou, Jianfeng
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 7(2020)
• Page Start: 1930
• Page End: 1943
• Publication Date: 2020-06
• Subjects: Sideslip angle estimation -- fusion methodology -- kinematic-based approach -- model-based approach -- extended Kalman filter -- experimental verification; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407019892156
• Languages: English
• ISSNs: 0954-4070

Abstract

This article introduces a reliable fusion methodology for vehicle sideslip angle estimation, which only needs the Controller Area Network–Bus signals of production vehicles and has good robustness to vehicle parameters, tire information, and road friction coefficient. The fusion methodology consists of two basic approaches: the kinematic-based approach and the model-based approach. The former is constructed into the extended Kalman filter for transient stage and large magnitude estimation, while the latter is designed to be an adaptive scheme for steady-state and small magnitude estimation. On this basis, combining the advantages of the two methods, a weight allocation strategy is proposed based on the front wheel steering angle and transient characteristics of lateral acceleration and yaw rate. The validity of the method is verified by simulation and experiment, and it is proved that the method can be effectively used for the sideslip angle estimation.