Current mission planning typically involves feed-forward strategies that do not adapt flight conditions and mission properties according to live vehicle power state updates. This results in autonomous flights that adhere to rigid rules for flight time, potentially missing opportunities to enhance mission range or improve recovery likelihood. To address these shortfalls, aircraft states and flight dynamics under varying conditions are experimentally characterized using a customized on-board data collection suite consisting of sensors and microprocessors. Post-processing is used to improve the quality of the data extracted from the sensors. A custom filtering window design provides timescale separation and filtering and cycle-synchronized averaging reduces noise in the data set. A linear vehicle power model is derived from the test data that describes operation in the neighborhood of stable cruising flight conditions. The vehicle power model is extended with models of the drive motors and battery to provide a framework for making mission-level predictions about the power requirements. The framework described is suitable for usage in adapting autonomous flight behaviors and mission planning to changing power availability.

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