Nested Power Models for Multirotor Propulsion: From Aerodynamic Drag to Electrical Losses

Traditional power models for multirotor drones only use speed to estimate aerodynamic losses. They ignore the extra power needed when the vehicle speeds up or slows down, so they give wrong results during rapid maneuvers. This work fixes that problem by building a step‑by‑step set of propulsion‑power models. The process begins with a basic aerodynamic power loss and then adds three more effects in order: * a reversible kinetic‑energy term that accounts for energy stored or released as the propeller speeds up or down, * a loss that depends on motor torque (electromechanical dissipation), and * a simple loss that grows with propeller speed. We collected data from a motor‑drive‑propeller

test rig. One group of tests was used to identify the model parameters, and a different group was used to check the predictions. Independent measurements of the rotor’s moment of inertia and its aerodynamic drag let us verify whether the models correctly split the total measured power into: * reversible kinetic‑energy exchange, and * irreversible losses (aerodynamic drag plus electromechanical friction). The results show that the kinetic‑energy term is necessary but not enough on its own to predict dynamic power accurately. Adding a loss term proportional to the square of motor torque gives the biggest boost in accuracy. Including the speed‑proportional loss prevents us from mistakenly counting irreversible losses as

kinetic‑energy exchange. Overall, this layered approach provides a solid, experimentally validated way to choose and apply dynamic propulsion‑power models in advanced UAV flight controllers and energy‑management simulations.

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