This article was first published by Noesis Solutions and can be downloaded here.
Engineers from Cybernet Systems took electric motor technology to higher quality and performance levels. Using Optimus from Noesis Solutions they optimized the design of an electric motor with interior permanent magnets (IPM) for maximum drive torque and minimum noise and vibration. Optimus enabled them to orchestrate ANSYS magnetic/structural simulations in exploring the design space and optimizing the motor design. The results of the multi-objective optimization were impressive: drive torque went up by 7% while lowering cogging torque by 35% and acoustic radiation by almost 6 dB. The approach opens up new opportunities for IPM synchronous electric motors used in (hybrid) electric vehicles, compressors and appliances.
Setting up the optimization process
Improving performance and perceived quality—The development of an IPM electric motor focuses on maximizing drive torque per ampere while minimizing cogging torque. The disrupting cogging torque phenomenon exhibits the symptoms of jerkiness, resulting in torque ripple at low motor speeds. To raise the perceived quality of the electric motor, the engineers also aimed at reducing the noise radiation by minimizing the radial rotor displacement. The development team also looked into making IPM motors more eco-friendly by avoiding the use of rare materials for the permanent magnets while increasing drive torque.
Automated simulation process flow—The transient 2D magnetic analysis of the 6-pole, 9-slot motor (ANSYS Maxwell) predicted the magnetic flux density and magnetic field loss, delivering drive and cogging torque values. Optimus automatically used the calculated magnetic forces as inputs for the subsequent 3D harmonic analysis on the IPM motor stator/rotor combination (ANSYS Workbench). This generated the maximum radial displacement of the rotor axis, from which the resulting acoustic radiation was calculated.
Setting design parameters and objectives—Most of the considered design parameters related to the size, orientation and positioning of the rotor magnets (width, thickness, angle, distance, material) and the stator coil slots (tip position, thickness). In addition, the engineers evaluated three distinct materials for the permanent magnets. The motor optimization objectives specified in Optimus included maximizing the drive torque (motor efficiency) and minimizing the radial rotor displacement (noise and vibration) and cogging torque (quality).
Exploring the design space
Early lessons through DOE analysis—The engineers first used design space exploration techniques to acquire up-front insight into the design potential. Using a Latin Hypercube DOE method available in Optimus, a set of 200 well-chosen virtual experiments was defined. Acting as a simulation robot, Optimus sampled the entire design space by automatically executing the experiments.
Correlation scatter diagrams—Correlation scatter diagrams revealed that drive torque is influenced most by magnet material, distance and width, whereas radial rotor displacement faces highest impact from the magnet width. Slot thickness plays a role in both mentioned optimization targets.
Selecting magnet material—Response surface modeling (RSM) in Optimus interpolated the DOE points to build a multidimensional response surface model (RSM) using the Kriging method. The RSMs showed how design targets are influenced by slot thickness and the permanent magnet material. The engineers opted for one of the two alternative magnet materials, as it became apparent that the material delivered substantially higher drive torque and offered sufficient potential to reduce cogging torque.
Multi-objective motor optimization—After the initial design space exploration, the engineers used Optimus to conduct a multi-objective electric motor optimization process—targeting maximum drive torque, minimum torque ripple, and minimum radial rotor displacement. They kept the same design parameter specifications as defined for the up-front DOE analysis. As constraints, the minimum value for drive torque was set to 11.4 Nm and the maximum value for cogging torque to 0.2 Nm. Using surrogate models, Optimus efficiently evaluated the performance of numerous motor design variants without requiring a full detailed analysis.
Balancing design objectives on the Pareto front
Optimizing toward Pareto front—The engineers selected the non-dominated sorting evolutionary algorithm (NSEA+) to execute the multi-objective global optimization and build the Pareto front. The points on this Pareto front all represent optimized motor design variants, each one featuring a different tradeoff between drive torque and maximum rotor displacement.
Balancing design objectives—The prioritization and balancing of design objectives ultimately decides which Pareto-optimal design (and corresponding trade-offs) will finally be selected by the engineering team.
For comparison purposes, the design parameter and objectives metrics for two distinct motor designs (number 3 and number 99) at opposing ends of the Pareto front are listed in tables. The tables show that variant 99 generates 7% higher drive torque, while drastically reducing cogging torque and maximum rotor displacement (35% and 44%, respectively). This results in a reduction of the sound pressure level by almost 6 dB. Alternatively, the sound reduction achieved by design variant 3 is even higher as well as the decrease in maximum rotor displacement. As a consequence, design 3 delivers less appealing improvement in maximizing drive torque and minimizing cogging torque.