Doctora en Ingeniería Eléctrica / Little work has been done on Battery Health Management (BHM) for rotary-wing Unmanned Aerial Vehicles (UAVs) despite the fact that they have become increasingly popular. They are highly maneuverable and enable both safe and low-cost experimentation in mapping, navigation, and testing of control algorithms in three dimensions. Also, they can perform maneuvers that cannot be achieved by their fixed-wing counterparts (e.g., hover in place, and take off and land vertically (VTOL)). However, small-size aircraft typically have weight, size and cost constraints. Thus, as small-size UAVs become more prevalent, the need for computationally efficient software will increase. This thesis proposes a holistic framework for the design, implementation and experimental validation of Battery Health Management (BHM) systems in small-size rotatory-wing Unmanned Aerial Vehicles (UAVs) that allows to accurately (i) estimate the State of Charge (SOC), and (ii) predict the End of Discharge (EOD) time of lithium-polymer batteries in small-size multirotors by using a model-based prognosis architecture that is efficient and feasible to implement in low-cost hardware. The proposed framework includes a simplified battery model that incorporate the electric load dependence, temperature dependence and SOC dependence by using the concept of Artificial Evolution to estimate some of its parameters, along with a novel Outer Feedback Correction Loop (OFCL) during the estimation stage which adjusts the variance of the process noise to diminish bias in Bayesian state estimation and helps to compensate problems associated with incorrect initial conditions in a non-observable dynamic system. Also, it provides an aerodynamic-based characterization of future power consumption profiles and utilizes a new definition of probability of failure to mitigate the risk. A quadrotor has been used as the validation platform. This thesis is the first research effort towards BHM for small-size rotary-wing UAVs validated beyond numerical simulations, and that addresses the problem from an efficient approach for constrained computing platforms. A proper prognosis of the EOD time is not only necessary to verify if the mission goal(s) can be accomplished but also essential to aid in online decision-making activities such as fault mitigation and mission replanning. Therefore, the results of this work will allow making decisions about the flight and having enough confidence in those decisions so that the mission objectives can be optimally achieved. Given that: (i) the flight times in battery-powered UAVs are indeed short, (ii) most flight plans are highly conservatives due to they suffer from uncertainties in estimating the remaining charge, (iii) applications in urban zones are limited due to the risk associated with accidental battery run-down during the flight, and (iv) UAVs are ideally suited for long endurance applications; it hopes the results of this research become a significant contribution to the battery-powered rotary-wing UAVs field. / Este trabajo ha sido parcialmente financiado por CONICYT bajo la beca CONICYT-PCHA/Doctorado Nacional /2014-63140178