Geometric Scaling Laws for Axial Flux Permanent Magnet Motors in In-Wheel Powertrain Topologies

TL;DR

This paper develops and validates geometric scaling laws for axial flux permanent magnet motors to optimize in-wheel powertrain designs, balancing efficiency, cost, and performance.

Contribution

It introduces analytical scaling models for AFMs based on RFMs, validated through simulations, enabling optimized electric vehicle powertrain configurations.

Findings

All-wheel drive with in-wheel AFMs is most efficient.

Scaling laws accurately predict motor parameters and losses.

Optimization reduces energy consumption across vehicle topologies.

Abstract

In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based on the scaling concept of RFMs from the literature, specifically deriving the model of the main loss component in electric motors: the copper losses. We further present separate scaling models of motor parameters, losses and thermal models, as well as the torque limits and cost, as a function of the design variables. Second, we validate these scaling laws with several experiments leveraging high-fidelity finite-element simulations. Finally, we define an optimization problem that minimizes the energy consumption over a drive cycle, optimizing the motor size and transmission ratio for a wide range of electric vehicle powertrain topologies. In our study, we observe that the all-wheel drive topology equipped with in-wheel AFMs is the most efficient, but also generates the highest material cost.