An Adaptive Vehicle Stability Control Algorithm Based on Tire Slip-Angle Estimation

2012 ◽  
Author(s):  
Mustafa Ali Arat ◽  
Kanwar Singh ◽  
Saied Taheri
Keyword(s):  
Control Algorithm ◽  
Stability Control ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Angle Estimation ◽  
Vehicle Stability Control

Adaptive Vehicle Stability Control With Optimal Tire Force Allocation

2012 ◽  
Author(s):  
Mustafa Ali Arat ◽  
Kanwar Bharat Singh ◽  
Saied Taheri
Keyword(s):  
Stability Control ◽  
Crash Risk ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Successful Implementation ◽  
Tire Force ◽  
Vehicle Stability Control ◽  
Saturation Region ◽  
Road Friction ◽  
Tire Forces

Vehicle stability control systems have been receiving increasing attention, especially over the past decade, owing to the advances in on-board electronics that enables successful implementation of complex algorithms. Another major reason for their increasing popularity lies in their effectiveness. Considering the studies that expose supporting results for reducing crash risk or fatality, organizations such as E.U. and NHTSA are taking steps to mandate the use of such safety systems on vehicles. The current technology has advanced in many aspects, and undoubtedly has improved vehicle stability as mentioned above; however there are still many areas of potential improvements. Especially being able to utilize information about tire-vehicle states (tire forces, tire-slip angle, and tire-road friction) would be significant due to the key role tires play in providing directional stability and control. This paper presents an adaptive vehicle stability controller that makes use of tire force and slip-angle information from an online tire monitoring system. Solving the optimality problem for the tire force allocation ensures that the control system does not push the tires into the saturation region where neither the driver nor the controller commands are implemented properly. The proposed control algorithm is implemented using MATLAB/CarSim® software packages. The performance of the system is evaluated under an evasive double lane change maneuver on high and low friction surfaces. The results indicate that the system can successfully stabilize the vehicle as well as adapting to the changes in surface conditions.

Advanced Active Safety System Using Separated Front and Rear Motor Control for a 4WD Hybrid Electric Vehicle

2007 ◽  
Vol 120 ◽  
pp. 223-228
Author(s):  
Dong Hyun Kim ◽  
Sung Ho Hwang ◽  
Hyun Soo Kim
Keyword(s):  
Motor Control ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Control Algorithm ◽  
Stability Control ◽  
Vehicle Stability ◽  
Vehicle Stability Control ◽  
Hybrid Electric ◽  
Stability Enhancement ◽  
Yaw Moment

Vehicle stability in 4 wheel drive(4WD) vehicles has been pursued by torque split based technology and brake based technology. The brake based methods are essentially brake maneuver strategies using the active control of the individual wheel brake. By comparison, the torque split based technologies realize stability by varying the traction torque split through powertrain to create an offset yaw moment. In the 4WD hybrid electric vehicle adopting separate front and rear motor, the vehicle stability enhancement algorithm using the rear motor control has some advantages such as faster response, braking energy recuperation, etc. However, since the left and right wheels are controlled by the same driving and regenerative torque from one motor, stability enhancement only by the front and rear motor control has a limitation in satisfying the required offset yaw moment. Therefore, to obtain the demanded offset yaw moment, a brake force distribution at each wheel is required. In this paper, a vehicle stability control logic using the front and rear motor and electrohydraulic brake(EHB) is proposed for a 4WD hybrid electric vehicle. A fuzzy control algorithm is suggested to compensate the error of the sideslip angle and the yaw rate by generating the direct yaw moment. Performance of the vehicle stability control algorithm is evaluated using ADAMS and MATLAB Simulink co-simulation.

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