Sideslip angle estimation using gps and disturbance accommodating multi-rate Kalman filter for electric vehicle stability control

2012 ◽  
Author(s):  
Binh Minh Nguyen ◽  
Yafei Wang ◽  
Hiroshi Fujimoto ◽  
Yoichi Hori
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Stability Control ◽  
Vehicle Stability ◽  
Sideslip Angle ◽  
Angle Estimation ◽  
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.

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