scholarly journals Nonlinear Body Slip Angle Observer for Electric Vehicle Stability Control

2008 ◽  
Vol 2 (4) ◽  
pp. 270-275 ◽  
Author(s):  
Cong Geng ◽  
Yoichi Hori
Keyword(s):  
Electric Vehicle ◽  
Stability Control ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Vehicle Stability Control

Mixed H∞/H2 Output Feedback Stability Control for Dual-Motor Independent Drive Electric Vehicle

2013 ◽  
Vol 658 ◽  
pp. 602-608 ◽  
Author(s):  
Cheng Lin ◽  
Chun Lei Peng
Keyword(s):  
Electric Vehicle ◽  
Output Feedback ◽  
Stability Control ◽  
Vehicle Stability ◽  
Robust Performance ◽  
Output Feedback Controller ◽  
Vehicle Stability Control ◽  
Feedback Stability ◽  
Simulation Results ◽  
Yaw Moment

This paper presents the design of mixed H∞/H2Output Feedback Controller for Independent Drive Electric Vehicle Stability Control. It generates yaw moment by applying driving intervention at front Independent driving wheels according to the vehicle states. The performance of the proposed controller is evaluated through a series of simulations under different velocity and different mass. The simulation results show that the controller can help vehicle against a certain range of uncertainty (speeds and loads) and get excellent robust performance.

scholarly journals Vehicle Stability Control Strategy Based on Recognition of Driver Turning Intention for Dual-Motor Drive Electric Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shu Wang ◽  
Xuan Zhao ◽  
Qiang Yu
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Stability Control ◽  
Motor Drive ◽  
Vehicle Stability ◽  
Lane Change ◽  
Vehicle Stability Control ◽  
Stability Control System ◽  
The Stability

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.

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.

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