UKF and EKF with time dependent measurement and model uncertainties for state estimation in heavy duty diesel engines
The continuous challenge to decrease emissions, sensor costs and fuel
consump- tion in diesel engines is battled in this thesis. To reach
higher goals in engine efficiency and environmental sustainability the
prediction of engine states is es- sential due to their importance in
engine control and diagnosis. Model output will be improved with help
from sensors, advanced mathematics and non linear Kalman
filtering. The task consist of constructing non linear Kalman Filters
and to adaptively weight measurements against model output to increase
estimation accuracy. This thesis shows an approach of how to improve
estimates by nonlinear Kalman filtering and how to achieve additional
information that can be used to acquire better accuracy when a sensor
fails or to replace existing sensors. The best performing Kalman
filter shows a decrease of the Root Mean Square Error of 75% in
comparison to model output.
Henrik Berggren and Martin Melin
2011
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
