Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach
This addresses the time-consuming design process for electrical machines, though it appears incremental as it combines existing techniques.
The paper tackles the computational intensity of magneto-static finite element simulations in optimizing permanent magnet synchronous machines by introducing a hybrid data-and physics-driven model, showing that it maintains Pareto result quality close to conventional methods while being computationally cheap.
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.