Estimating the Tire-Road Friction Coefficient Based on Tire Force Measurements

Electronic stability control (ESC) has been shown to reduce the accident rate in a number of situations. Therefore, it is important to keep improving ESC and other control systems. One way to do this is by improving the available information on the state of the vehicle and its environment. The tire-road friction coefficient µ is particularly important, as it limits the maximum possible tire forces. Literature shows that systems like ESC, ABS, torque vectoring and adaptive cruise control all benefit from knowledge of µ. In this study, a new estimation method is proposed to estimate µ, using tire forces. The method works in the non-linear region of the tire and for combined slip conditions. For each tire, µ is estimated using an extended Kalman filter, together with a magic formula tire model. The algorithm then selects the estimate with the lowest uncertainty. The method is developed in Matlab, and evaluated with a CarSim vehicle model under braking, steady-state cornering and for a double lane change. The results show that this estimator is promising first step. It succeeds to estimate µ when the tires are at their limit, but is less successful at lower slip levels. An explanation is that the effect of µ on the slip-slope is not present in CarSim. In the real world, a relationship exists between µ and the slip-slope, so that the estimator has the potential to work for lower levels of slip. Therefore, the proposed next step is to evaluate this estimation method on a real vehicle.

Subject

tire-road friction coefficient
extended Kalman filter
on-line parameter estimatio
vehicle dynamics

To reference this document use:

http://resolver.tudelft.nl/uuid:3d5ee83c-a35e-4141-8408-ab3916ca1c8a

Embargo date

2014-07-12

Part of collection

Student theses

Document type

master thesis

Rights

Files

PDF

Thesis_Johannes_van_Ginkel.pdf

923.81 KB

Close viewer