A study of electric vehicle suspension control system based on an improved half-vehicle model

2007 ◽  
Vol 4 (3) ◽  
pp. 236-242 ◽  
Author(s):  
Jiang-Tao Cao ◽  
Hong-Hai Liu ◽  
Ping Li ◽  
David J. Brown ◽  
Georgi Dimirovski
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Vehicle Suspension ◽  
Vehicle Model ◽  
Suspension Control

Dynamic Modeling and Acceleration Control of Electric Vehicles

2012 ◽  
Vol 476-478 ◽  
pp. 944-948 ◽  
Author(s):  
Xiao Long Liu ◽  
Shao Peng Zhu ◽  
Zhi Jun Wu
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Speed Control ◽  
Acceleration Process ◽  
Vehicle Model ◽  
Brushless Dc ◽  
Wheel Drive ◽  
Speed Control System ◽  
Four Wheel Drive ◽  
Motor Model

This paper constructs a dynamic model of a four-wheel drive electric vehicle, which contains a vehicle model and a brushless DC motor model. In order to improve the starting and acceleration performance of the electric vehicle, we design a speed and current double closed-loop speed control system based on the constructed dynamic electric vehicle model. The starting and acceleration process of the electric vehicle is simulated and analyzed by CarSim-Matlab/Simulink co-simulation. The effectiveness of the speed control system is evaluated by the co-simulation results. In addition, the robustness of the speed control system is also analyzed for different vehicle masses.

Simultaneous estimation of state and unknown road roughness input for vehicle suspension control system based on discrete Kalman filter

2019 ◽  
Vol 234 (6) ◽  
pp. 1610-1622
Author(s):  
Gi-Woo Kim ◽  
Sun-Woo Kang ◽  
Jung-Sik Kim ◽  
Jong-Seok Oh
Keyword(s):  
Kalman Filter ◽  
Control System ◽  
The State ◽  
Vehicle Suspension ◽  
Simultaneous Estimation ◽  
Unknown Input ◽  
State Variables ◽  
The Road ◽  
Road Roughness ◽  
Suspension Control

This study presents an improved discrete Kalman filter for simultaneously estimating both all state variables and the unknown road roughness input for a vehicle suspension control system that plays a key role in the ride quality and handling performance while driving the vehicle. The suspension system is influenced by the road roughness input, which causes undesirable vibrations associated with vehicle instability. It is therefore important to estimate the road roughness and state variables information when designing the model-based controller for the vehicle suspension control system associated with the vehicle’s vertical dynamics. However, the implementation of conventional estimation theories for the suspension control system is challenging because the road roughness acts as an unknown input and is difficult to be measured or estimated while driving. This study presents an improved Kalman filter with unknown input, which can simultaneously estimate the state variables and road roughness without any prior information about the vehicle suspension control system. The proposed road roughness input estimator is evaluated by using an in-vehicle test bed with a laser-type profilometer. Finally, the state estimation performance of the proposed estimator for a vehicle suspension control system is validated by using CarSim software.

Design and Simulation of a Stability Control System for 4 Independent Wheel Drive Electric Vehicle

2016 ◽  
Author(s):  
Juan S. Núñez ◽  
Luis E. Muñoz
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Sliding Mode ◽  
Stability Control ◽  
Performance Comparison ◽  
Phase Portraits ◽  
Vehicle Model ◽  
Stability Control System ◽  
The Stability ◽  
Yaw Moment

With the aim of prevent situations of vehicle instability against different driving maneuvers, the vehicle yaw stability becomes crucial for safe operation. This paper presents the design and simulation of a traction and a stability control system algorithms for independent four-wheel-driven electric vehicle. The stability control system consists of a multilevel algorithm divided into a high level controller and a low level controller. First, an analysis of the stability of the vehicle is performed using phase portraits analysis, both in open loop and closed loop. The stability control system is designed to generate a desired yaw moment according to the steady state cornering relationship with the steering angle input. As the test vehicle, a 14 DoF vehicle model is proposed including nonlinear tire models that allow the generation of combined forces. The vehicle model includes the powertrain dynamics. The yaw moment generation is performed using the traction and braking forces between the tires of each side of both front and rear axle. In order to generate the maximum traction forces in each of the wheels, a traction and a braking control is developed via a sliding mode controller scheme. Finally a performance comparison between a controlled and an uncontrolled vehicle is presented. The behavior of both vehicles is simulated using a classical double lane change driving maneuver.

Study on the Semi-Active Suspension Control System and Its Stability Based on T-S Fuzzy Neural Control Strategy

2004 ◽  
Vol 471-472 ◽  
pp. 557-562
Author(s):  
Chen Long ◽  
Hao Bin Jiang ◽  
M.C. Yang
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Neural Control ◽  
Active Suspension ◽  
Vehicle Suspension ◽  
Robust Performance ◽  
Suspension Control ◽  
Fuzzy Neural ◽  
The Stability ◽  
Suspension Model

A semi-active vehicle suspension model is built, and semi-active suspension control system based on T-S fuzzy neural control strategy is designed. Then, the stability of the control system is analyzed and the condition of stability of the system is deduced. Simulations and experiments are carried out and their results accord with each other, which shows that the controller is stable, valid and has strong robust performance.

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.

scholarly journals Optimization and Enhancement of Charging Control System of Electric Vehicle Using MATLAB SIMULINK

2021 ◽  
Vol 1105 (1) ◽  
pp. 012004
Author(s):  
R H Ali Faris ◽  
A A Ibrahim ◽  
N B Mohamad wasel ◽  
M M Abdulwahid ◽  
M F Mosleh
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Matlab Simulink ◽  
Charging Control
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