Stability Control of Electric Vehicles with In-wheel Motors

electric vehicle;vehicle dynamics;soft computing;stability control;slip control;torque vectoring;active steering;driver model;Mechanical Engineering

Jalali, Kiumars

University of Waterloo

关键词: electric vehicle;    vehicle dynamics;    soft computing;    stability control;    slip control;    torque vectoring;    active steering;    driver model;    Mechanical Engineering;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/5268/1/Jalali_Kiumars.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Recently, mostly due to global warming concerns and high oil prices, electric vehicles have attracted a great deal of interest as an elegant solution to environmental and energy problems. In addition to the fact that electric vehicles have no tailpipe emissions and are more efficient than internal combustion engine vehicles, they represent more versatile platforms on which to apply advanced motion control techniques, since motor torque and speed can be generated and controlled quickly and precisely.The chassis control systems developed today are distinguished by the way the individual subsystems work in order to provide vehicle stability and control. However, the optimum driving dynamics can only be achieved when the tire forces on all wheels and in all three directions can be influenced and controlled precisely. This level of control requires that the vehicle is equipped with various chassis control systems that are integrated and networked together. Drive-by-wire electric vehicles with in-wheel motors provide the ideal platform for developing the required control system in such a situation.The focus of this thesis is to develop effective control strategies to improve driving dynamics and safety based on the philosophy of individually monitoring and controlling the tire forces on each wheel. A two-passenger electric all-wheel-drive urban vehicle (AUTO21EV) with four direct-drive in-wheel motors and an active steering system is designed and developed in this work. Based on this platform, an advanced fuzzy slip control system, a genetic fuzzy yaw moment controller, an advanced torque vectoring controller, and a genetic fuzzy active steering controller are developed, and the performance and effectiveness of each is evaluated using some standard test maneuvers. Finally, these control systems are integrated with each other by taking advantage of the strengths of each chassis control system and by distributing the required control effort between the in-wheel motors and the active steering system. The performance and effectiveness of the integrated control approach is evaluated and compared to the individual stability control systems, again based on some predefined standard test maneuvers.