Optimal Design of Electric Micromobility Vehicles

TL;DR

This paper develops a modeling and optimization framework for designing cost-effective electric micromobility vehicles, considering their powertrain components and operational scenarios to optimize total cost of ownership.

Contribution

It introduces a novel optimization approach for joint design and control of electric micromobility vehicles, accounting for nonlinearities and vehicle mass variations.

Findings

Vehicle operating environment significantly influences optimal design.

Regenerative braking and gear-changing may not be cost-effective.

Framework validated through high-fidelity simulations.

Abstract

This paper presents a modeling and optimization framework to design battery electric micromobility vehicles, minimizing their total cost of ownership (TCO). Specifically, we first identify a model of the electric powertrain of an e-scooter and an e-moped consisting of a battery, a single electric motor and a transmission. Second, we frame an optimal joint design and control problem minimizing the TCO of the vehicles. Since this problem is nonlinear w.r.t. the motor size and the total mass of the vehicle, but convex if their value is given, we efficiently solve the problem for a range of motor sizes with an algorithm based on second-order conic programming iterating on the vehicle's mass. Finally, we showcase our framework on custom-created driving cycles for both vehicles on hilly and flat scenarios, providing an in-depth analysis of the results and a numerical validation with high-fidelity simulations. Our results show that the characteristics of the area where the vehicles are employed have a significant impact on their optimal design, whilst revealing that regenerative braking and gear-changing capabilities (as in the case of a continuously variable transmission) may not be worth implementing.