D. Moorman
Moser Energy Systems,
United States
Keywords: AI, machine learning, Precision sustainment, Predictive logistics, hybrid controls, digital twin
Summary:
Contested environments necessitate efficient, reliable, and economical utilization of resources. The fuel and power systems used to power the growing reliance on energy for communications, surveillance, armament, mobility, medical treatment, and food and shelter present critical logistical support challenges. These challenges become more difficult as distance increases between the supply base and the area of operations. “Precision sustainment” and “Predictive logistics” are two of the most common terms used to describe the logistical needs of modern warfare. When these terms are applied to fuel and power systems, it means that every drop of fuel and piece of equipment must be managed to the highest efficiency and effectiveness. Wasted fuel and compromised equipment with degraded performance or equipment nearing the end of its service life cannot be sustained in an environment that is exposed to risk, costly to transport, and powers equipment vital to the warfighter’s mission. This presentation shows the enabling technologies combined in a novel approach that fundamentally changes the deployment, servicing, and support of critical power systems. Hybrid technology (engine-driven power systems with energy storage) has greatly enhanced power system reliability, resiliency, and efficacy. Batteries are used to provide power at the lower and upper edges of the load profile improving fuel efficiency by as much as 90%, enabling a more robust response to loading and unloading events, and allowing for stealth-mode operation reducing noise and heat signatures. Advanced diagnostics and “smart” microgrid controls coupled with the energy storage used on a hybrid power system provide a controllable load that can be connected to the power system’s prime mover. Data is measured, stored, and compared during consistent loading and unloading events made possible by the available capacity of the energy storage, the precise energy analysis provided by advanced power electronics, and the measurement of relevant engine diagnostics. Intelligent Diagnostic Screening (IDS) performance tests are evaluated and compared against the system’s baseline digital twin and with past IDS tests to better inform predictive maintenance events, overhaul schedules, and end-of-life tracking. IDS tests are scheduled into the normal operating functions performed by the power system without impacting the application or functionality. IDS testing commands load to the prime mover. The battery used for the hybrid power system provides a controlled load ensuring that the available charging capacity of the battery is adequate to absorb the output of the generator. The inverter and associated controls precisely measure specific power output data providing a relative and measured response to power commands. The prime mover is instrumented with various measuring devices to identify engine performance, stability, thermal efficiency, mechanical efficiency, and vibration. The value of each measured parameter would be used to establish the “score” on that particular engine and compared to the stored digital twin for that power system to track advancement through the life cycle accurately.