Synthetic Optimization of Trafficability and Roll Stability for Off-Road Vehicles Based on Wheel-Hub Drive Motors and Semi-Active Suspension

Xiang Fu, Jiaqi Wan, Daoyuan Liu, Song Huang, Sen Wu, Zexuan Liu, Jijie Wang, Qianfeng Ruan and Tianqi Yang ()
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Xiang Fu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jiaqi Wan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Daoyuan Liu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Song Huang: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Sen Wu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Zexuan Liu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jijie Wang: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Qianfeng Ruan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Tianqi Yang: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2024, vol. 12, issue 12, 1-29

Abstract: Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel’s slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary.

Keywords: off-road vehicle; rollover stability; slip-ratio control; LuGre tire model; model predictive control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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