Efficient Multi-Physics Optimization of High-Speed EV Motors: Employing a Hybrid, Adaptive Surrogate-Assisted Strategy

The optimal design of high-speed electric vehicle (EV) motors requires simultaneously addressing both electromagnetic (EM) performance and mechanical stress (MS). Reducing the significant computational time associated with this multi-physics optimization, however, is a fundamental challenge. To address this challenge, this paper presents a hybrid, two-step adaptive strategy. As performing finite element analysis (FEA) for all physics is computationally burdensome, the proposed strategy applies a surrogate model exclusively to the MS analysis, while continuing to use FEA for EM analysis. The proposed process first employs an initially imprecise surrogate model as a statistical filter to intelligently narrow the design space, and then transitions to a full surrogate replacement for the expensive MS FEA evaluations once the model achieves high fidelity. This strategy was applied to the design of a high-speed EV propulsion motor using a particle swarm optimization algorithm. To validate its efficacy, the method was benchmarked against a baseline method that omits the initial adaptive filtering step. The results demonstrate a substantial reduction in total computational time while successfully yielding a final design.