Title :
Adaptive Fuzzy Controller for Hybrid Traction Control System based on Automatic Road Identification
Author :
Zhang, Jianlong ; Chen, Deling ; Yin, Chengliang
Author_Institution :
Inst. of Automobile Eng., Shanghai Jiao Tong Univ.
Abstract :
In the normal condition, the front wheels follow the control trace of the driver and rear wheels follow the direction of the vehicle. The vehicle will spin and lose the control trace of the driver if the traction force is greater than the friction force. Therefore, a vehicle should maintain an adequate slip ratio of the tires and follow the control trace of the driver. This paper describes a fuzzy controller for hybrid traction control system in hybrid electric vehicles (HEVs) that prevents the spinning of the drive wheels during take-off and acceleration through targeted, brief brake impulses in motor torque. The task is to have the fuzzy supervisory controller generate the electric brake torque, for motor of a HEV. The electric brake torque is treated as reference input regenerative braking torque, for lower level control modules. When these lower level motor controller tracks its reference input, the desired slip ratio, can be reduced. Emergency lane change and tire slip ratio change simulations are performed to show the effectiveness of the control. The efficiency and easy implementation of the fuzzy controller lead to the conclusion that fuzzy logic is an adequate and promising framework for hybrid traction control system in hybrid electric vehicles.
Keywords :
adaptive control; brakes; control system synthesis; friction; fuzzy control; hybrid electric vehicles; internal combustion engines; machine control; mechanical contact; road vehicles; torque; traction; tyres; wheels; adaptive fuzzy controller; automatic road identification; brake impulses; electric brake torque; friction force; fuzzy logic; fuzzy supervisory controller; hybrid electric vehicles; hybrid traction control system; motor torque; slip ratio; tires; traction force; wheels; Adaptive control; Automatic control; Control systems; Fuzzy control; Fuzzy systems; Hybrid electric vehicles; Programmable control; Torque control; Vehicle driving; Wheels;
Conference_Titel :
Automation Science and Engineering, 2006. CASE '06. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
1-4244-0310-3
Electronic_ISBN :
1-4244-0311-1
DOI :
10.1109/COASE.2006.326936