A critical component of many vehicle control systems such as stability control and lateral control systems is the accurate knowledge of vehicle sideslip and yaw rate. Sideslip is not measured on most production vehicles so this key vehicle state has to be estimated. This lab's method for estimating these key vehicle states and sensor biases is by combining Global Positioning System (GPS) with an Internal Navigation System (INS). Two Kalman filters, a model based filter and a Kinematic filter, are used to integrate the INS sensors with GPS heading and velocity to provide a high update rate of the vehicle states and sensor biases. Additional key vehicle parameters, such as tire-cornering stiffness, are identified and used to correct the model based estimator.
Work is also being performed on estimation of vehicle parameters and fuel economy performance using the heavy trucks at Auburn University 's NCAT test track. During the inaugural run of the NCAT asphalt test track, fuel economy was seen to decrease as the test period elapsed. When fuel economy was compared to the measured track roughness (measured in the International Roughness Index or IRI) it appeared that as roughness increased, fuel economy decreased. The proposed fuel economy research is intended to prove if there is a difference in fuel consumption, not only with asphalt degradation over time, but between different asphalt mixes. Currently GPS, INS, and wheel speed sensor measurements are being combined with engine data to estimate rolling resistance and fuel use on the various sections of the test track. Estimating the change in rolling resistance on each section of track is a key component for performing the fuel economy correlation analysis. Knowing various asphalts properties, rolling resistance, and engine data on each track section will allow correlating fuel economy with asphalt composition, construction, IRI, and surface friction.
Research Assistants
- Rusty Anderson
- Matt Heffernan
Sponsors
Publications:
- Anderson, R., and Bevly, D.M., Estimation of Slip Angles Using a Model Based Estimator and GPS
Links: