Optimizing Mid-Drive Electric Motor Performance and Cost for Electric Bike Applications

The widespread adoption of electric vehicles is influenced by factors including consumer preferences, cost reduction in battery technology, and environmental regulations. Within this context, electric micro-mobility emerges as a crucial sector, providing sustainable transportation options in urban settings central to the efficacy of such micro-mobility solutions lies the electric motor. This study focuses on designing and optimizing a 12-slot/10-pole flat magnet motor configuration for e-bikes, tailored to a unique driving cycle that captures dynamic load profiles, computed with a backward model from real-world scenarios, encompassing factors such as rolling resistance, aerodynamics, climbing, and acceleration. A multi-objective genetic algorithm optimization, integrated with MATLAB and Ansys Motor-CAD, is employed for comprehensive optimization considering constraints like permanent magnet demagnetization, torque ripple, mechanical stresses, and weight. Additionally, a multiphysics analysis validates the optimized design, highlighting its applicability beyond micro-mobility to other fields utilizing permanent-magnet synchronous motors.