Finite-Element-Based Computationally Efficient Scalable Electric Machine Model Suitable for Electrified Powertrain Simulation and Optimization

Abstract

Electric machines are a key component of electric/hybrid electric vehicle (EV/HEV) powertrains. Thus, computationally efficient models for electric machines are essential for powertrain-level design, simulation, and optimization. In this paper, a finite-element-based method for quickly generating torque-speed curves and efficiency maps for electric machines is presented. First, magnetostatic finite-element analysis (FEA) is conducted on a “base” machine design. This analysis produces torque, normalized losses, flux linkage, and the maximum magnetic field intensity in the permanent magnets for a wide range of current magnitudes and phase angles. These values are then scaled based upon changing the size of the machine and the effective number of turns of the machine windings to quickly generate a variety of new machine designs and their corresponding efficiency maps using postprocessing techniques. Results suggest that, by avoiding resolving the FEA for the scaled designs, the proposed techniques can be used to quickly generate efficiency maps, and thus are useful for EV/HEV powertrain-level simulation and optimization.

13 Figures and Tables

Cite this paper

@article{Zhou2015FiniteElementBasedCE, title={Finite-Element-Based Computationally Efficient Scalable Electric Machine Model Suitable for Electrified Powertrain Simulation and Optimization}, author={Kan Zhou and Andrej Ivanco and Zoran S. Filipi and Heath F. Hofmann}, journal={IEEE Transactions on Industry Applications}, year={2015}, volume={51}, pages={4435-4445} }