  Fuel consumption of heavy-duty vehicles such as tractors, bulldozers etc. is
comparably high due to their scope of operation. The operation settings are
usually fixed and not tuned to the environmental factors, such as ground
conditions. Yet exactly the ground-to-propelling-unit properties are decisive
in energy efficiency. Optimizing the latter would require a means of
identifying those properties. This is the central matter of the current study.
More specifically, the goal is to estimate the ground conditions from the
available measurements, such as drive train signals, and to establish a map of
those. The ground condition parameters are estimated using an adaptive
unscented Kalman filter. A case study is provided with the actual and estimated
ground condition maps. Such a mapping can be seen as a crucial milestone in
optimal operation control of heavy-duty vehicles.
