Dynamic Modeling and State Estimation for Multi-In-Wheel-Motor-Driven Intelligent Vehicle Wuhan University of Technology

Format:

Conference/Event

Author/Creator:

Lin, Lin, author.

Contributor:

Guo, Xuexun

Pei, Xiaofei

Yang, Bo

Zhang, Yanggang

Conference Name:

Intelligent and Connected Vehicles Symposium (2017-09-26 : Kunshan City, Jiangsu, China)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractDynamic modeling and state estimation are significant in the trajectory tracking and stability control of the intelligent vehicle. In order to meet the requirement of the stability control of the eight-in-wheel-motor-driven intelligent vehicle, a full vehicle dynamics model with 12 degrees of freedom, including the longitudinal, lateral, yaw and roll motion of the body, and rotational motion of 8 wheels, is established for the research of the intelligent vehicle in this paper. By simulation with MATLAB/SIMULINK and by comparison with the TruckSim software, the reliability and practicality of the dynamics model are verified. Based on the established dynamics model, an extended Kalman filter (EKF) state observer is proposed to estimate the vehicle sideslip angle, roll angle and yaw rate, which are the key parameters to the stability control of the intelligent vehicle. The accuracy and effectiveness of the EKF state observer are evaluated and validated through co-simulation between MATLAB/ SIMULINK and TruckSim. The results show the proposed EKF observer can effectively filter the noise and has high accuracy in estimating the vehicle sideslip angle, roll angle and yaw rate

Notes:

Vendor supplied data

Publisher Number:

2017-01-1996

Access Restriction:

Restricted for use by site license