Effective Scaling of High-Fidelity Electric Motor Models for Electric Powertrain Design Optimization

TL;DR

This paper presents a method for efficiently optimizing high-fidelity electric motor designs for automotive powertrains using proportional scaling and Bayesian optimization, leading to improved energy efficiency.

Contribution

It introduces a novel framework combining proportional scaling of motor components with derivative-free optimization for vehicle-level design improvement.

Findings

Energy consumption reduced by 0.13% in a city car scenario

Framework effectively integrates high-fidelity motor models with vehicle simulations

Demonstrates potential for more accurate and efficient motor design optimization

Abstract

In general, electric motor design procedures for automotive applications go through expensive trial-and-error processes or use simplified models that linearly stretch the efficiency map. In this paper, we explore the possibility of efficiently optimizing the motor design directly, using high-fidelity simulation software and derivative-free optimization solvers. In particular, we proportionally scale an already existing electric motor design in axial and radial direction, as well as the sizes of the magnets and slots separately, in commercial motor design software. We encapsulate this motor model in a vehicle model together with the transmission, simulate a candidate design on a drive cycle, and find an optimum through a Bayesian optimization solver. We showcase our framework on a small city car, and observe an energy consumption reduction of 0.13% with respect to a completely proportional scaling method, with a motor that is equipped with relatively shorter but wider magnets and slots. In the extended version of this paper, we include a comparison with the linear models, and add experiments on different drive cycles and vehicle types.