Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric AircraftPredicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, high-confidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.
Document ID
20190001777
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Bole, Brian (Stinger Ghaffarian Technologies Inc. (SGT Inc.) Moffett Field, CA, United States)
Daigle, Matthew (NASA Ames Research Center Moffett Field, CA, United States)
Gorospe, George (Stinger Ghaffarian Technologies Inc. (SGT Inc.) Moffett Field, CA, United States)
Date Acquired
March 22, 2019
Publication Date
July 8, 2014
Subject Category
Aircraft Propulsion And PowerAircraft Design, Testing And Performance
Report/Patent Number
ARC-E-DAA-TN16213Report Number: ARC-E-DAA-TN16213
Meeting Information
Meeting: European Conference of the PHM Society
Location: Nantes
Country: France
Start Date: July 8, 2014
End Date: July 10, 2014
Sponsors: Prognostics and Health Management (PHM) Society