A Geometric Electric Motor Model for Optimal Vehicle Family Design

This paper presents a design optimization framework that jointly optimizes battery size with the geometric dimensions of the electric motor for a family of battery electric vehicles, with global optimality guarantees. As opposed to conventional models, we devise a quasi-static model of the motor internal losses as a function of both its geometry and operating points, using a convex surrogate modeling approach. Specifically, we implement a low-level motor scaling, capturing the impact on performance and losses of changing the motor geometry in axial and radial directions. Hence, we leverage the framework to solve a concurrent optimization problem and identify the optimal module sizing for a family of electric vehicles. Finally, we test our framework on a benchmark problem where we jointly design motor and battery for three different types of vehicles (a city car, a compact car, and a cross over), whereby the prediction efficiency is in line with the high-fidelity modeling software.