Abstract
A lateral acceleration is considered to be a significant sensor signal for an estimation of a side slip angle. Due to the fact that a characteristic of a lateral G sensor, the sensor has a technical issue when a road bank angle has presented. In order to resolve the issue, this paper describes a novel method for the real time estimation of a vehicle side slip angle and a road bank angle simultaneously. A Bayesian tracking approach is used to estimate the road bank angle by comparing a measured lateral acceleration with the calculated one in the case of various angle. A Kalman Filter has been implemented through bicycle model using vehicle roll angle, road bank angle and angular velocity of side slip angle. The performance of the proposed estimation method has been evaluated via vehicle tests on a real road.
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Abbreviations
- ay.mea :
-
measured lateral acceleration
- Vx :
-
longitudinal velocity
- m :
-
mass of vehicle
- ß:
-
side-slip angle
- ß:
-
side-slip angular velocity
- ß:
-
yaw rate
- C f :
-
front cornering stiffness
- C r :
-
rear cornering stiffness
- l f :
-
distance from center of mass to front axle
- l r :
-
distance from center of mass to rear axle
- I x :
-
roll inertia
- I z :
-
yaw inertia
- d f :
-
front steering angle
- A :
-
system matrix
- B :
-
input matrix
- ϕRoll :
-
roll angle
- ϕBank :
-
bank angle
- g :
-
gravitational acceleration
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Hyun, M., Cho, W. Estimation of Road Bank Angle and Vehicle Side Slip Angle Using Bayesian Tracking and Kalman Filter Approach. Int.J Automot. Technol. 19, 993–1000 (2018). https://doi.org/10.1007/s12239-018-0096-y
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DOI: https://doi.org/10.1007/s12239-018-0096-y