This study details the development and experimental validation of a high-fidelity
one-dimensional (1D) simulation model for a two-speed transmission designed for
off-road vehicles, such as tractors and backhoe loaders used in agricultural and
civil engineering applications. The model, implemented in the AMESim platform
from Siemens, integrates physics-based loss sub-models for all major components,
including gears, bearings, seals, and fluid drag (churning) losses. After
development, the model was rigorously validated against test bench data, with
efficiency measurements taken across various speed, torque, and oil level
combinations, demonstrating a strong correlation with experimental results. A
detailed analysis enabled the quantification of the contribution of each loss
mechanism, identifying the countershaft gears and input shaft bearings as the
primary contributors. Furthermore, a Machine Learning (ML)–based calibration
framework, employing Bayesian Optimization, was implemented to reduce
discrepancies between simulation and experiment and to generate a synthetic
dataset for the creation of fast-executing surrogate models. The study concludes
that the proposed methodology constitutes an effective tool for efficiency
analysis and optimization during early design stages, establishing a foundation
for future integration with ML techniques and the development of digital
twins.
