Drag Prediction of an Electric Cargo Scooter

Drag prediction is a very important stage in the design of an electric cargo scooter. Early knowledge of the drag forces will enable designers to accurately estimate the motor power, size, battery pack size, vehicle range, and the maximum speed of the vehicle. In the present paper we present a simulation-based methodology to estimate the drag forces and drag coefficient accurately and to identify possible design changes to optimize and reduce them. The main advantage of simulation-based method is reducing the number of prototypes built during the initial design stages and reducing wind tunnel tests. Future variants also will not require any wind tunnel measurements as they can be accurately predicted using the established process. Reynolds Averaged Navier Stokes (RANS)-based steady state Computational method is utilized. The study includes mesh independence, inlet velocity independence, and Turbulence model independence.