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

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Brian Bole Matthew Daigle George Gorospe

Abstract

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, highconfidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.

How to Cite

Bole, B., Daigle, M., & Gorospe, G. (2014). Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1535
Abstract 53 | PDF Downloads 36

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Keywords

Battery discharge prognostics, Unscented Kalman Filtering, Unmanned Aerial Vehicle, Uncertainty Bounds, Flight Plan Evaluation

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Section
Technical Papers