Huang, Chun-Cheng (Eindhoven University of Technology), van Kampen, Jorn (Eindhoven University of Technology), Salazar, Mauro (Eindhoven University of Technology)

A Two-Dimensional Spatial Optimization Framework for Electric Motorcycle Powertrains

Scheduled for presentation during the Regular Session "Modelling and optimal design of electric powertrains" (WeB1), Wednesday, June 18, 2025, 15:00−15:20, Kapel

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

This study presents a framework that focuses on optimizing the two-dimensional (2D) placement of electric motorcycle powertrain components, accounting for position, rotation, alignment, and shape complexity. It is part of a broader integrated framework aimed at optimizing both energy consumption and drivability. First, we construct a 2D placement model at the component level and include constraints such as orientation, shape composition, and overlap detection. Second, we introduce mixed-integer convex relaxation techniques implemented throughout the research. Finally, we demonstrate our framework on two electric motorcycle topologies. The results show that our framework autonomously generates precise layout solutions, accounting for complex shape compositions and non-orthogonal orientations. Furthermore, the higher complexity placement improves quantified drivability by 2.5% compared to the benchmark placement found in existing electric motorcycles.