Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach

Vivek Parekh, Dominik Flore, Sebastian Schöps, Peter Theisinger
6/15/2023
cs.LG

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Abstract

Magneto-static finite element (FE) simulations make numerical optimization of electrical machines very time-consuming and computationally intensive during the design stage. In this paper, we present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM). Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM by predicting intermediate FE measures. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators (KPIs), e.g., torque, shaft power, and material costs. We perform multi-objective optimization with both classical FE and a hybrid approach using a nature-inspired evolutionary algorithm. We show quantitatively that the hybrid approach maintains the quality of Pareto results better or close to conventional FE simulation-based optimization while being computationally very cheap.

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Cite this paper

@article{parekh2023multiobjective,
  title  = {Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach},
  author = {Vivek Parekh and Dominik Flore and Sebastian Schöps and Peter Theisinger},
  year   = {2023},
  eprint = {2306.09096},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.09096v1}
}

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