This paper presents a novel battery health management technology for the new generation of electric unmanned aerial vehicles powered by long-life, high-density, scalable power sources. Current reliability based techniques are insufficient to manage the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The technique presented here encodes the basic electrochemical processes of a Lithium-polymer battery in an advanced Bayesian inference framework to simultaneously track battery state-of-charge as well as tune the battery model to make accurate predictions of remaining useful life. Results from ground tests with emulated flight profiles are presented with discussions on the use of such prognostics results for decision making.

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