Cloud-Based Identification of Dynamic Trailer States

A current trend in the commercial vehicle industry are autonomous trucks and tractor-semitrailers which will lead to an increasing automation of functions and autonomous transport processes in the future. In this contribution, we present the IdenT system concept, which has been developed for tractor-semitrailers within the research project of the same name, consisting of an intelligent trailer sensor network, a cloud-based data platform and methods for on- and offline data processing. In this work, we focus on the offline process, by presenting its architecture and main functions. As fundamental elements of the offline process, the digital twin of the trailer, i.e., a detailed multi-body model with current inputs and parameters acquired via the system’s online setup, and the methodology to identify the road profile are presented in detail. We show and discuss results of the offline process for some demonstration cases that illustrate how the simulation of the trailer digital twin with identified road profiles can provide a valuable identification of the trailer’s real system dynamics. Moreover, some expected limits of the approach and results of some representative signals are discussed for the demonstration cases.