  This paper instantiates a convex electric powertrain design optimization
framework, bridging the gap between high-level powertrain sizing and low-level
components design. We focus on the electric motor and transmission of electric
vehicles, using a scalable convex motor model based on surrogate modeling
techniques. Specifically, we first select relevant motor design variables and
evaluate high-fidelity samples according to a predefined sampling plan. Second,
using the sample data, we identify a convex model of the motor, which predicts
its losses as a function of the operating point and the design parameters. We
also identify models of the remaining components of the powertrain, namely a
battery and a fixed-gear transmission. Third, we frame the minimum-energy
consumption design problem over a drive cycle as a second-order conic program
that can be efficiently solved with optimality guarantees. Finally, we showcase
our framework in a case study for a compact family car and compute the optimal
motor design and transmission ratio. We validate the accuracy of our models
with a high-fidelity simulation tool and calculate the drift in battery energy
consumption. We show that our model can capture the optimal operating line and
the error in battery energy consumption is low. Overall, our framework can
provide electric motor design experts with useful starting points for further
design optimization.
