Optimizing battery life for electric UAVs using a Bayesian framework

The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. For electric UAVs (unmanned aerial vehicles) the variation in the load profile can be very unpredictable. This paper presents a model parameter augmented Particle Filtering prognostic framework to explore battery behavior under these future load uncertainties. Stochastic programming schemes are explored to utilize the battery life predictions generated as a function of load, in order to infer the most optimal flight profile that would maximize the battery charge utilized while constraining the probability of a dead stick condition (i.e. battery shut off in flight).