Development of a HUMS for UAV hybrid power system using digital twin and AI techniques

Abstract

Traditional aeronautical power systems, typically based on fossil fuels, present a series of limitations, including: i) the added weight associated with onboard fuel storage, ii) limited endurance due to fuel consumption, and iii) the emission of atmospheric pollutants. These constraints become particularly critical in application scenarios where extended endurance and environmental sustainability are key requirements. A notable example is represented by high-altitude, long-endurance (HALE) unmanned aerial vehicles (UAVs), whose deployment is rapidly expanding due to their suitability for a wide range of missions, including surveillance, environmental monitoring, and long-range communications. To enable the technological advancement of such platforms, alternative power generation architectures must be explored. In this context, hybrid electric power systems, integrating solar panels, lithium-ion batteries, and fuel cells, offer a promising solution. Solar and fuel cell subsystems ensure stable and continuous power generation over extended periods, including during night-time operations, while lithium-ion batteries provide high-power bursts during transient phases such as take-off, landing, or auxiliary system activation. Nevertheless, the use of these hybrid systems introduces unique challenges in terms of safety, reliability, and system complexity. They must operate in harsh environmental conditions and often require remote monitoring capabilities that enable condition-based intervention without interrupting critical missions. To address these challenges, this paper presents a Health and Usage Monitoring System (HUMS) for a hybrid power system composed of a solar panel, a lithium-ion battery, and a fuel cell, developed through the integration of digital twin modeling and artificial intelligence (AI) techniques. In particular, AI data-driven methods provide a powerful and flexible framework for monitoring the complex system composed of multiple energy sources. However, to achieve reliable performance, they require large and representative datasets, which are often unfeasible to obtain experimentally. To overcome this limitation, a digital twin of the hybrid power system is developed in the MATLAB/Simulink environment and used to simulate system behavior under both healthy and faulty conditions. The resulting synthetic data are then employed to train diagnostic/prognostic algorithms. This approach offers an efficient and scalable solution for implementing intelligent health monitoring in hybrid power systems, enhancing reliability, autonomy, and operational availability in long-endurance UAV applications.