Shape and Topology Optimization of Electrical Machines using Lie Derivative-Based Analytical Sensitivity Analysis

The paper addresses the optimal design of electric machines, through the general setting of both shape and topology optimization. The optimization problems are efficiently solved with a classical gradient-based mathematical programming algorithm. An analytical sensitivity analysis for the nonlinear magnetostatic problem that can handle both shape and topology design variables, based on the Lie derivative is derived and applied to the optimal design of an interior permanent magnet (IPM) machine.