Two-wheel-driven Electric Superbike Powertrain Optimization

TL;DR

The paper addresses energy-efficient design for a two-wheel-driven electric superbike by jointly optimizing gear ratio, propulsion force distribution (propulsive blending), and rear motor and battery sizing. It introduces a scalable motor loss model and an energy-based objective $J = E_c + w \sum_{t=1}^T F_{\mathrm{tr}}^{\mathrm{f}}$, solved as a nonlinear program via CasADi/IPOPT, subject to performance and adhesion constraints. Key contributions include defining the regenerative ratio $\zeta$ and average efficiency $\eta$ to evaluate energy recuperation, and demonstrating through driving-cycle case studies that joint optimization reduces energy consumption (up to 22.36% for the Sport cycle) and enables lighter, cheaper powertrains. The framework provides a practical design methodology for high-performance electric motorcycles, with implications for motor/battery sizing and control strategies under real-world cycling and safety requirements, and suggests future extensions to include costs and lateral-dynamics constraints.

Abstract

In this paper, we propose an optimization framework for the powertrain design of a two-wheel-driven electric superbike, minimizing energy consumption. Specifically, we jointly optimize the force distribution between the wheels with the gear ratio, and rear motor and battery sizing while explicitly considering vehicle dynamics and performance constraints. First, we present an energy consumption model of the vehicle, including a scalable model of the electric machine based on data from the industry, accounting for iron, copper, and mechanical losses. Then, we analyze the propulsive blending strategy to distribute the required power to the wheels while considering adherence limits. Finally, we demonstrate the effectiveness of our approach by analyzing the design of a superbike, based on regulatory driving cycles and a custom high-performance circuit by comparing the force distribution approaches. The results underline the significance of joint optimization of powertrain components and propulsive bias, achieving a reduction of up to 22.36% in energy consumption for the Sport high-performance driving cycle.

Figures (1)

• Figure 3: A 20-second window of the Sport drive cycle, showing the speed profile followed by the vehicle at the top, the power repartition with the optimal propulsive strategy $\sigma^\star$ in the middle, and the comparison between the two different strategies $\sigma^\mathrm{a}$ and $\sigma^\star$ at the bottom.