A method of online traction parameter identification and mapping

TL;DR

This paper presents an adaptive unscented Kalman filter method to identify and map ground conditions affecting fuel efficiency in heavy-duty vehicles, enabling better operation control based on environmental factors.

Contribution

It introduces a novel approach for real-time ground condition estimation and mapping using drive train signals, enhancing vehicle operation optimization.

Findings

Successful ground condition mapping demonstrated in case study

Improved accuracy of ground property estimation over traditional methods

Potential for real-time adaptive control in heavy-duty vehicle operation

Abstract

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.