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Dynamic Modeling and State Estimation for Multi-In-Wheel-Motor-Driven Intelligent Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 23, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Dynamic 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.
CitationLin, Z., Guo, X., Pei, X., Yang, B. et al., "Dynamic Modeling and State Estimation for Multi-In-Wheel-Motor-Driven Intelligent Vehicle," SAE Technical Paper 2017-01-1996, 2017, https://doi.org/10.4271/2017-01-1996.
Data Sets - Support Documents
|Unnamed Dataset 1|
- Kwak , B. , Park , Y. , and Kim , D. Design of Observer for Vehicle Stability Control System SAE Technical Paper 2000-05-0230 2000
- Zhao , P. , Zong , C. , Hu , D. , Zheng , H. et al. Numerical Achieved Extended Kalman Filter State Observer Design Based on a Vehicle Model Containing UniTire Model SAE Technical Paper 2008-01-1783 2008 10.4271/2008-01-1783
- Chen , Y. , Ji , Y. , and Guo , K. A Sliding Mode Observer for Vehicle Slip Angle and Tire Force Estimation SAE Technical Paper 2014-01-0865 2014 10.4271/2014-01-0865
- Chaudhuri , S. , Saini , V. , and Singh , M. Kinematic Analysis of Multi-Axle Steering System for Articulated Vehicle SAE Technical Paper 2009-26-0067 2009 10.4271/2009-26-0067
- Xiong , L. , Chen , C. , Feng , Y. Modeling of Distributed Drive Electric Vehicle Based on Co-simulation of Carsim/Simulink Journal of System Simulation 26 2014 1143 1155
- Stephant , J. , Charara , A. , Meizel , D. Evaluation of a sliding mode observer for vehicle sideslip angle Control Engineering Practice 15 2007 803 812
- Dakhlallah J. , Glaser S. , Mammar S. and Sebsadji Y Tire road forces estimation using extended Kalman filter and sideslip angle evaluation Proceedings of the 2008 American Control Conference Washington USA June 2008
- Chen , G. , Zong , C. , and Guo , X. Traction Control Logic Based on Extended Kalman Filter for Omni-directional Electric Vehicle SAE Technical Paper 2012-01-0251 2012 10.4271/2012-01-0251
- Rezaeian , A. , Zarringhalam , R. , Fallah , S. , Melek , W. et al. Cascaded Dual Extended Kalman Filter for Combined Vehicle State Estimation and Parameter Identification SAE Technical Paper 2013-01-0691 2013 10.4271/2013-01-0691
- LI L , Jia G , Ran X et al. A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road Vehicle System Dynamics 2014 52 2 280 308
- Huang Y. , Bao C. and Wu J. Estimation of Sideslip Angle Based on Extended Kalman Filter Journal of Electrical and Computer Engineering 2017 2017 1 9