Reluctance Magnetic Gears (RMGs) represent a cost-effective alternative to conventional magnetic gears, replacing the inner rotor permanent magnets with a toothed ferromagnetic rotor and adopting rectangular instead of arc-shaped magnets on the outer rotor. While these design choices reduce manufacturing complexity and material costs, they inherently introduce higher torque ripple, making simultaneous optimization of average torque and ripple a critical and non-trivial task. In this work, a multi-objective genetic algorithm is applied to four RMG configurations with integer gear ratios GRint equal to 4, 5, 6, and 7, with a fixed inner rotor tooth number n3 equal to 5. Seven design variables are optimized simultaneously: five radial thicknesses and two fill factors. The resulting Pareto fronts quantify the trade-off between average torque and ripple for each configuration. Analysis of the optimal solutions reveals a consistent geometric allocation pattern across all gear ratios, suggesting the existence of a common optimization criterion potentially generalizable to other RMG configurations. The influence of the gear ratio on both torque performance and optimal parameter distribution is discussed in detail.
Roscioli et al. (2026) studied this question.