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PROJECT TITLE :

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

ABSTRACT:

Electrical machines are a key element of electrical/hybrid electric vehicle (EV/HEV) powertrains. Thus, computationally efficient models for electrical machines are essential for powertrain-level style, simulation, and optimization. During this paper, a finite-element-primarily based methodology for quickly generating torque–speed curves and potency maps for electrical machines is presented. First, magnetostatic finite-part analysis (FEA) is conducted on a “base” machine design. This analysis produces torque, normalized losses, flux linkage, and the most magnetic field intensity within the permanent magnets for a wide range of current magnitudes and section angles. These values are then scaled based mostly upon changing the dimensions of the machine and the effective range of turns of the machine windings to quickly generate a selection of new machine styles and their corresponding potency maps using postprocessing techniques. Results counsel that, by avoiding resolving the FEA for the scaled designs, the proposed techniques will be used to quickly generate potency maps, and thus are useful for EV/HEV powertrain-level simulation and optimization.


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