Libo Chao

Integrative braking control system for electric vehicles

For Electric Vehicle (EV), energy saving and endurance mileage prolonging are very important. Regenerative braking techniques are key parts of electric techniques for saving energy. The traditional braking system must be updated to adapt the condition that the friction braking force is coexisting with the motor regenerative braking force. For the integrative braking control system it is so important to keep the traditional braking performance while recovering braking energy and fully exert the capability of the motor. In this paper, a new integrative braking control system will be studied. The simulation results based on the ADVISOR and the HIL test bench validate the effects of the system, such as energy recovering and braking performance.

Traction control algorithm based on fuzzy PID and PWM modulation

In this paper, a traction control algorithm based on fuzzy PID and modulation of pump based on PWM is proposed. The target brake pressure needed to be exerted on the driving wheel is determined by a fuzzy PID controller from the difference between the target wheel speeds and actual wheel speeds and is fulfilled by PWM control of hydraulic pump of the hydraulic control unit. The control algorithm is validated based on a hardware Hardware-in-the-loop (HIL) test bench which is based on the xPC Target™ product of MATLAB®, and the test results of show that the algorithm can control the spin of driven wheels effectively.

Design of longitudinal vehicle velocity observer using fuzzy logic and Kalman filter

An adaptive vehicle longitudinal velocity observer is designed in this paper for Electronic stability control system based on the standard sensors for ESC. At first, wheel speed of each wheel is converted to the center of gravity of the vehicle after they have been converted to free rolling speed; then fuzzy logic is used to get a first estimation of vehicle longitudinal velocity; together with the estimated vehicle longitudinal acceleration, Kalman filter is used the estimate the velocity of vehicle for the use of ESC control. Simulation results show that the algorithm proposed can estimate the vehicle longitudinal velocity precisely.

Vehicle stability control algorithm based on optimal fuzzy theory

As a new active safety technology, Vehicle Stability Control (VSC) system can maintain the vehicle stability by dominating the yaw moment effectively under extreme driving conditions. This paper presents a novel VSC strategy devoted to prevent vehicles from spinning and drifting out, which is based on the Linear Quadratic Regulator (LQR) and the fuzzy control theory. The response of the vehicle can follow the ideal output of the 2-DOF reference model through controlling the yaw rate and side slip angle. The performance of the proposed algorithm is evaluated under various emergency maneuvers and road conditions. The simulation results indicate that the proposed system can significantly improve vehicle lateral stability for active safety, which has strong adaptability and robustness.