ABSTRACT
Omni-directional
electric vehicle built by our research group
is an advanced electric vehicle whose four wheels can drive,
steer and brake independently. The vehicle chassis system is
composed of four in-wheel motors, four independent steer
motors and electromagnetic brake system, and its control
system is divided into logical control layer and underlying
execution layer. The information exchange between these two
layers is implemented by CAN bus. In this paper, the traction
control logic for Omni-directional electric vehicle is
developed. The study mainly involves two aspects: the
vehicle states estimation and the traction control logic design.
The vehicle states, including vehicle longitudinal velocity,
lateral speed, side slip angle and yaw rate, etc, are estimated
based on Extended Kalman Estimation and multiple degrees
of freedom vehicle model. Longitudinal velocity is used to
design the traction control logic; the other estimated states are
the preconditions for the future study. To design the traction
control logic, the slip ratio of each wheel is obtained
according to the estimated velocity firstly. Through PID
control, the single wheel slip ratio control is realized to make
the slip ratio of each wheel below the optimum value. Then
the vehicle traction control logic is introduced to eliminate
the influence of the additional yaw moment to steer stability.
Finally, the effectiveness of the estimation algorithm and the
traction control logic are verified by the simulation in the
environment of Matlab/simulink and Carsim.
INTRODUCTION
Omni-directional electric vehicle (EV) is a new concept
vehicle, each wheel of the EV can drive, steer and brake
independently. On this advanced platform, the flexible
driving and turning mode can be implemented easily, such as
zero radius turning, oblique driving and crab. The chassissystem widely uses x-by-wire technology. It includes the
drive
system housing four hub-motors in the wheels, the
steering system turning by four torque motors, and the brake
system using electromagnetic brakes. All these actuators can
be controlled independently. Due to their rapid response and
precise control, high performance of chassis control such as
traction control, electronic differential control, direct yaw
moment control and regenerative braking control systems can
be employed easily at low cost. Omni-directional EV
represents the development way of advanced civilian vehicle,
military vehicle and wheel mobile robots in the future [ 1, 2].
Like
traditional vehicles, the vehicle stability and safety are
important for Omni-directional EV. Traction control system,
as an important component to improve vehicle stability, can
enhance a driver's ability to maneuver a vehicle moving in a
low adhesion road or during starting. In these conditions, the
wheel drive torque is likely to exceed the maximum adhesion
force between the vehicle's tires and the road, which causes
excessive slip [ 2, 10, 11]. While the wheel slip ratio is much
larger than the optimal slip ratio of the corresponding road,
the longitudinal adhesion force will decrease greatly and the
wheel's steer-ability will be lose during cornering maneuvers.
So the traction control system for Omni-directional EV is
necessary to be studied.
Because all wheels of Omni-directional EV are driven, the
rotational speeds of the wheels cannot be used to estimate the
speed of the vehicle. To solve this problem, many new
methods [ 3, 4, 5, 6] are proposed. Some methods [ 3, 4, 5] can
maximize
the adhesion force without vehicle speed and road
condition. They only need the rotating speed of the driving
wheels and the motor torques. Other methods [ 6] use a
specific observer to estimate vehicle states including speed
information.
Traction Control Logic Based on Extended Kalman
Filter for Omni-directional Electric Vehicle2012-01-0251
Published
04/16/2012
Guoying Chen, Changfu Zong and Xueli Guo
Jilin University
Copyright © 2012 SAE International
doi:10.4
SAE_2012-01-0251_Traction Control Logic Based on Extended Kalman Filter for Omni-directional Electric Vehicle
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