Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft

Brian Bole, Matthew Daigle, and George Gorospe
Submission Type: 
Full Paper
Supporting Agencies (optional): 
NASA Ames Research Center
phmce_14_059.pdf1.21 MBJune 10, 2014 - 9:46am

Predicting 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.

Publication Year: 
Publication Volume: 
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Submission Keywords: 
Battery discharge prognostics; Unscented Kalman Filtering; Unmanned Aerial Vehicle
Uncertainty Bounds
Flight Plan Evaluation
Submission Topic Areas: 
Component-level PHM
Model-based methods for fault detection, diagnostics, and prognosis
Uncertainty Quantification and Management in PHM
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