Optimizing Multiphase Hairpin Windings with a Two-Level Approach for High-Efficiency and Highpower Density Aircraft Electric Propulsion

This paper introduces a two-level optimization strategy to maximize power density and efficiency in multiphase hairpin winding electric motors for aircraft propulsion. The first stage employs a Multi-Objective Genetic Algorithm (MOGA) to optimize key design parameters, including winding geometry, slot-pole combinations, and the configuration of a five-stage Halbach PM array, achieving enhanced electromagnetic performance. In the second stage, a Genetic Algorithm (GA) uses the updated motor constant (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K_{m}{ }^{u p}$</tex>) as a fitness function to minimize copper losses and refine winding and stator slot designs. By implementing this approach, a 1 MW permanent magnet motor achieves an exceptional power density of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$35-40 \text{kW} / \text{kg}$</tex> (active mass) and an efficiency exceeding 98 %. These results demonstrate the effectiveness of the proposed framework in developing high-performance, reliable electric propulsion systems for next-generation aircraft.