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Automotive Engineering ›› 2025, Vol. 47 ›› Issue (11): 2238-2249.doi: 10.19562/j.chinasae.qcgc.2025.11.017

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Estimation of Front Wheel Steering Angle for Vehicle Steer-by-Wire System Considering Tire Cornering Characteristics

Xiaoqiang Sun(),Jiawei Ding,Haoran Tang,Yingfeng Cai,Long Chen   

  1. Institute of Automotive Engineering,Jiangsu University,Zhenjiang 212013
  • Received:2025-02-14 Revised:2025-03-22 Online:2025-11-25 Published:2025-11-28
  • Contact: Xiaoqiang Sun E-mail:sxq@ujs.edu.cn

Abstract:

The steering angle of the front wheels is the primary tracking control objective in vehicle steer-by-wire (SBW) system, and its accurate estimation is a critical aspect of the redundancy design in such systems. For the problem that traditional methods fail to ensure the estimation accuracy of the front wheel steering angle across a wide range of driving conditions, an estimation method for the front wheel steering angle in SBW systems is proposed, which takes into account of the nonlinear characteristics of tire lateral deflection. Firstly, a two-degree-of-freedom yaw-roll vehicle dynamics model and a SBW system model are constructed, followed by the completion of piecewise affine (PWA) identification for the tire nonlinear cornering characteristics. Subsequently, the state equation of the PWA system is derived, and a front wheel steering angle estimation strategy for the vehicle SBW system is designed using the maximum correlation square root cubature Kalman filter (MCSCKF) algorithm to enhance the estimation accuracy during extensive state transitions. Finally, a co-simulation validation platform for the performance estimation of the front wheel steering angle in vehicle SBW systems is established based on CarSim and Simulink. The effectiveness of the front wheel steering angle estimation is verified in conjunction with two typical operational conditions. The results show that under sinusoidal steering conditions, the MCSCKF algorithm has a maximum reduction in estimation error of 66.3% and 41.1% compared to EKF and MCEKF algorithms, respectively. Under the dual lane steering condition, the MCSCKF algorithm has a maximum reduction in estimation error of 64.3% and 38.2% compared to the EKF and MCEKF algorithms, respectively, which verifies that the proposed method can effectively improve the accuracy of front wheel steering angle estimation under a wide range of driving conditions for automobiles.

Key words: steer-by-wire system, front wheel steering angle, tire cornering characteristics, piecewise affine identification, maximum correlation entropy square root volume Kalman filter