Papers for

electric motor engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Multirotor power models improve accuracy by adding torque and inertia effects

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

Abstract: Speed-only aerodynamic power models for multirotor propulsion cannot represent acceleration-dependent effects. This work develops a nested sequence of propulsion-power models that starts from aerodynamic power dissipation and progressively introduces a reversible kinetic-energy rate, torque-dependent electromechanical dissipation, and lumped speed-proportional dissipation. The models are identified using one subset of experiments and validated using the other on a motor-drive-propeller unit. Independent estimates of rotational inertia and aerodynamic drag complement predictive validation by assessing whether the models correctly attribute the measured power to reversible kinetic-energy exchange and irreversible dissipation and, within the latter, to aerodynamic and electromechanical losses. The results show that the reversible kinetic-energy rate is necessary but insufficient for accurate dynamic power prediction. Dissipation proportional to the squared motor torque provides the main additional improvement, while speed-proportional dissipation further prevents irreversible losses from being attributed to reversible kinetic-energy exchange. The resulting methodology provides a reusable and experimentally verifiable basis for developing and selecting dynamic propulsion-power models for multirotor systems.

Tue 6 OctRobotics
The gist
Predicting how much power a drone’s motors use is tricky because many factors affect it, like speed, acceleration, and electrical losses. The authors developed a series of models that start simply by measuring air drag but then add how energy builds up and releases during acceleration and how the motor’s torque affects losses. They tested these models on a real motor and propeller setup and showed that including torque-related losses and kinetic energy changes leads to better power use predictions. This helps understand where power is wasted and can guide better drone design and control.
Open → 2610.08177v1