scholarly journals Artificial Intelligence Application’s for 4WD Electric Vehicle Control System

2012 ◽  
Vol 03 (03) ◽  
pp. 243-250 ◽  
Author(s):  
Abdelfatah Nasri ◽  
Brahim Gasbaoui
Keyword(s):  
Artificial Intelligence ◽  
Control System ◽  
Electric Vehicle ◽  
Vehicle Control

Effect of Inner and Outer Wheels Driving Force Control on Small Electric Vehicle

2020 ◽  
Vol 17 (4) ◽  
pp. 8246-8254
Author(s):  
K. Shibazaki ◽  
H. Nozaki
Keyword(s):  
Control System ◽  
Angular Velocity ◽  
Electric Vehicle ◽  
Force Constant ◽  
Force Control ◽  
Driving Force ◽  
Vehicle Control ◽  
Steering Angle ◽  
Force Control System ◽  
The Difference

In this study, in order to improve steering stability during turning, we devised an inner and outer wheel driving force control system that is based on the steering angle and steering angular velocity, and verified its effectiveness via running tests. In the driving force control system based on steering angle, the inner wheel driving force is weakened in proportion to the steering angle during a turn, and the difference in driving force is applied to the inner and outer wheels by strengthening the outer wheel driving force. In the driving force control (based on steering angular velocity), the value obtained by multiplying the driving force constant and the steering angular velocity,  that differentiates the driver steering input during turning output as the driving force of the inner and outer wheels. By controlling the driving force of the inner and outer wheels, it reduces the maximum steering angle by 40 deg and it became possible to improve the cornering marginal performance and improve the steering stability at the J-turn. In the pylon slalom it reduces the maximum steering angle by 45 deg and it became possible to improve the responsiveness of the vehicle. Control by steering angle is effective during steady turning, while control by steering angular velocity is effective during sharp turning. The inner and outer wheel driving force control are expected to further improve steering stability.

3305 Proposal of emergent hybrid electric vehicle control system for time-axis variation of driving state

2012 ◽  
Vol 2012.22 (0) ◽  
pp. _3305-1_-_3305-8_
Author(s):  
Taku YAMAZAKI ◽  
Satoru FURUGORI ◽  
Yasuhide KURODA ◽  
Takamasa SUETOMI ◽  
Takahide NOUZAWA ◽  
...  
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Vehicle Control ◽  
Time Axis ◽  
Hybrid Electric

scholarly journals Research on the Electric Vehicle Control System

2015 ◽  
Vol 8 (8) ◽  
pp. 103-110 ◽  
Author(s):  
Haiying Wang ◽  
Tianjun Sun ◽  
Xingbo Zhou ◽  
Qi Fan
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Vehicle Control

Design and Implementation of Self-Balancing Electric Vehicle Control System

2015 ◽  
Vol 738-739 ◽  
pp. 950-954
Author(s):  
Ben Sheng Qi ◽  
Kang Wang ◽  
Xuan Xuan Xiao ◽  
Hong Xia Miao
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Lagrange Equation ◽  
Linear Quadratic Regulator ◽  
Optimization Method ◽  
Vehicle Control ◽  
Linear Quadratic ◽  
Design And Implementation ◽  
Vehicle System ◽  
The Mathematical Model

In order to further optimize the control system of self-balancing electric vehicle, the method of linear quadratic regulator (LQR) based on genetic algorithm (GA) was presented in this paper. Firstly, the mathematical model of self-balancing electric vehicle was established by Lagrange equation, and then matrix Q and R in LQR which is used to control self-balancing electric vehicle system were optimized by GA. Thus the optimal control of self-balancing electric vehicle control system was realized. The optimization method was proved to be effective by comparing the simulation results of the optimized controller with the original.

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