An investigation on the effects of key input parameters to electric bicycle for high performance

Abstract

An investigation on the effects of key input parameters to electric bicycle for high performance Department of Mechanical Engineering Le Trong Hieu The purpose of this research is to study how the operating and structure parameter affect the dynamic, require power and electric consumption of electric bicycles (EBs). To achieve this goal, a simulation model was established through MATLAB Simulink software to investigate dynamic and electricity consumption characteristics. Based on the established mathematical models, the motion, dynamic and electric consumption of the EB are analyzed and optimized under the effects of frontal area, bicycle mass, wheel radius, and sprocket transmission ratio. On the other hand, to improve bicycle performance, the research applied an artificial neural network and genetic algorithm (ANN-GA) to forecast the bicycle performance and identify its optimal power demand. The MATLAB-Simulink model created 1000 data points, which are utilized for training, testing, verifying the ANN model. The ANN model is developed with transmission ratio, frontal area, wheel radius, bicycle velocity as an input, and power demand and battery voltage as an output parameter. After the ANN is trained, it is applied into the genetic algorithm to identify the optimal value. The study showed that the electric bicycle configuration can achieve optimal power 546.3 W at 30.7 km/h under speed level_5, wheel radius of 0.42 m, frontal area 0.423 m2. Besides that, the experimental test was conducted on real road test at Taehwa river to verify the simulated results. The experimental and simulation results have the same trend under the same conditions.

Keywords: electric bicycle model, electric consumption, artificial neural network, genetic algorithm, effective performance area.