Vehicle dynamic state observation and rolling resistance estimation via unknown input adaptive high gain observer

Abstract

Amongst all these resistive forces in a modern passenger car, depending on the driving conditions and tire specifications, about 20–30% of total fuel consumption is related to rolling resistance. The rolling resistance of the vehicle is still rather unknown and research should be conducted to be able to estimate it reliably and accurately. This accuracy is particularly necessary at the scale of a single vehicle to reduce the fuel consumption, considering that the rolling resistance coefficient is a dimensionless quantity with an order of magnitude of 1 to 2%. The purpose of this paper is to develop an unknown input adaptive gain estimation algorithm for the accurate estimation of the rolling resistance of a vehicle. This is motivated by the fact that the adaptive solution is the most suitable for the rolling resistance estimation because of variable dynamics and continuous change in a situation during the real driving situation. Their robustness to modelling error, parameter uncertainty and input noise used to detect the variation in the input. The developed approach is validated experimentally with a relative mean error of less than 10%. The experiments were done on University Gustave Eiffel test tracks.