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Prognostic Algorithm Performance Demonstrated on Electric UAV Batteries Onboard the Edge 540 Platform

Prognostic health management algorithms designed by the Prognostics Center of Excellence, and implemented on a PC104 embedded hardware system developed by teams at Langley Research Center (LaRC) and Dryden Flight Research Center (DFRC), were successfully flown onboard an Edge 540 electric UAV on July 22, 2011. During this flight the health management system monitored the battery voltages, currents, and temperatures at run time and estimated the true state of-charge (SOC) of the batteries in-flight, while managing the uncertainties in the models, measurements, and loads, and correctly predicted the remaining useful life (flight time) to within two minutes over multiple 15-20 minute flights.

BACKGROUND: Electric unmanned aerial vehicles (UAVs) have become the new face of green aviation. They are being increasingly deployed in military, civilian, and scientific missions all over the globe. However, battery-powered electric UAVs suffer from uncertainties in estimating the remaining charge and hence most flight plans are highly conservative in nature. Batteries decrease in capacity with time and usage. Various factors like ambient storage temperatures and the SOC at which the battery was stored affect capacity fade. Additionally, the amount of usable charge of a battery for a given discharge profile is not only dependent on the starting SOC, but also on other factors like battery health and the discharge or load profile imposed. This problem is more pronounced in battery-powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements, and a dead stick condition (battery shutoff in flight) can have catastrophic consequences. A reliable battery life prediction, integrated with the decisioning process of the operator/pilot, can prevent such mishaps while optimizing operational efficiency. Recent flight tests showed that the battery health management (BHM) system gave the UAV pilots enough confidence to extend the flight times by as much as 33.33% over conventional flight plans.

Prognostic algorithms are being developed under the AvSafe System-Wide Safety and Assurance Technologies (SSAT) program. The Edge 540 is a flight vehicle at NASA Langley that has been used to answer a number of different research questions. After suffering a mishap due to poor subjective assessment of the remaining battery life, teams at Langley, Dryden, and Ames collaborated to avoid a similar mishap through the implementation of an onboard battery health management system. The flight test mentioned above served to validate some of the research hypotheses made during prognostic algorithm development while at the same time addressing a real need of remaining life estimation of the energy storage device on the UAV.

NASA PROGRAM FUNDING: SSAT

COLLABORATORS: Patrick Quach (LaRC), Sixto Vasquez (LaRC), Ed Hogge (LaRC), Tom Strom (LaRC), Boyd Hill (LaRC), Ed Koshimoto (DFRC), James Murray (DFRC), Mike Venti (DFRC), and Kai Goebel (ARC).

Contact: Bhaskar Saha

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Prognostic Algorithm Performance Demonstrated on Electric UAV Batteries Onboard the Edge 540 Platform
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