Multi-Fidelity Model-Based Size Optimization of Electric Machines

Abstract

The multi-modal problem and high computational cost represent challenges in the optimization of electric machines owing to their highly nonlinear electromagnetic response. To overcome these challenges, this paper proposes a multi-fidelity model-based sequential optimization method in which both low- and high-fidelity models are employed in two phases. In phase 1, the reluctance network (RN) is adopted as the low-fidelity model and mainly contributes to alleviating the abovementioned challenges. To overcome the low accuracy of the RN, the optimal design is obtained using a finite element model (FEM) in phase 2. The multi-start strategy and gradient-based algorithm are utilized instead of a heuristic algorithm in all phases to avoid excessive calculations. This multi-fidelity model concept presents a novelty compared to previous research that focused only on algorithm development. The effectiveness of the proposed method is validated with two examples, consisting of the TEAM workshop problem 25 and the torque ripple minimization of an interior permanent magnet synchronous motor. The optimal designs and computational time resulting from the proposed method are compared with those of the conventional method, where only a FEM is used during optimization. The results show that the proposed method is remarkable in finding superior optimal designs, while ignoring unimportant local optima. Additionally, it can save up to 90% of the computational time required by the conventional method.