Affiliation:
1. School of Mechanical Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Abstract
The purpose of this study was to enhance electric scooter performance utilizing a novel method consisting of an artificial neural network (ANN) and genetic algorithm (GA) to predict power demand, battery voltage, and identify the optimal performance range. For training, validation, and testing, a dataset comprising 1000 data points for each parameter was extracted from a MATLAB-Simulink model. The ANN application was used to identify the battery voltage and power demand, reflecting the simulated results under varying key input parameters. Additionally, the GA was used to identify the optimal performance after the ANN had been trained. The results showed that the ES can achieve a speed of 28.2 km/h while using an optimal power of 553 W, at a wind velocity of 0 m/s, a slope ratio of 0%, and a wheel diameter of 0.37 m. The achieved results show that the ANN-GA method is appropriate for determining the operating and structural parameters for maximizing the performance of electric scooters. To support the simulated results, an experimental study was carried out with an actual road test along the Taehwa river.
Funder
Ministry of Trade, Industry and Energy
Ministry of Educatio
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction