Use of digital twins in hardware-in-the-loop modeling of transport systems
DOI:
https://doi.org/10.34121/1028-9763-2026-3-89-102Keywords:
digital twin, hardware-in-the-loop (HIL) modeling, transport systems, tracked vehicles, static digital twin, dynamic digital twin, predictive digital twin, intelligent digital twin, intelligent-dynamic digital twinAbstract
The paper examines the use of digital twins in hardware-in-the-loop (HIL) modeling of transport systems with a particular focus on tracked vehicles. The relevance of the study is determined by the growing complexity of modern transport objects, the need to improve the accuracy of dynamic modeling, reduce the cost of full-scale tests, and support decision-making under near real-time conditions. The aim of the research is to systematize the types of digital twins, analyze their structural and functional features, and justify the choice of the most appropriate structure for HIL-based vehicle modeling. The study uses methods of comparative analysis of recent scientific publications, structural generalization, and a systematic comparison of digital twin types based on criteria including interaction with the physical object, real-time operation, predictive capability, adaptability, and control support. A classification of digital twins for HIL tasks is proposed, including static, dynamic, predictive, intelligent, intelligent-dynamic, and cognitive types. For each type, the main functions, structural components, the level of integration with the HIL system, and potential field of application are identified. It is shown that static digital twins are suitable as a basic level of digital representation: dynamic twins ensure real-time behaviour reproduction; predictive twins are intended for predicting future states and failures; intelligent twins provide adaptive analytics and decision support; and cognitive twins represent a promising but still insufficiently mature direction for real-time HIL applications. The study substantiates that the intelligent-dynamic digital twin is the most appropriate architecture for HIL modeling of transport systems because it combines detailed dynamic modeling, real-time synchronization, adaptive analysis, prediction, and control decision generation. Tabl.: 2. Figs.: 6. Refs.: 35 titles.
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