Accurately estimating the time of battery end of discharge (EOD) in electric unmanned aerial vehicles (UAVs) provides assurance that a given mission can be completed before the energy stored in the battery runs out and aids decision-making processes such as mission replanning to mitigate shortcomings associated with the available energy. The accuracy of the predicted battery EOD time is strongly correlated to the accuracy of the expected power consumption during the mission. This paper reports on a novel model-based framework for power consumption prognosis in multirotors which includes an improved power consumption model that characterizes the power required by a multirotor in axial and nonaxial translation and incorporates the wind effects on the required power. A particle filter is used in conjunction with the concept of artificial evolution to estimate and monitor wind speed, wind direction, and thrust based on measurements of power. Monte Carlo sampling-based predictor is used to predict the trajectories of power used in battery EOD prognostic. The framework is applied to battery EOD prognostic of a quadcopter that performs a delivery mission with low horizontal speed (where rotor tilt is not a significant factor). Results show that predicted trajectories of power accurately represent the uncertainty of future power consumption. Even when certain information (such as aircraft weight) is not available at every time-step, the framework allows tracking the actual EOD time because of its capability to monitor thrust. These results demonstrate the effectiveness of the proposed framework. © 2020 American Society of Mechanical Engineers (ASME). All rights reserved.