<p>A three-wheeled electric scooter (3WES) with two control techniques is modeled and simulated in this study. The conventional direct torque control (C-DTC) and the DTC based on a neural network artificial multi layers (ANN-DTC). The objective is to assess the traction system's response to the control approach. by 3WES taking into account the dynamics of the scooter, the range and the energy consumption of the battery. The 3WES was simulated numerically using the MATLAB/Simulink environment, which is powered 1.5 kW by two induction motors integrated into the rear wheels. Where the reference speeds of the rear wheels detected using a differential electronic. This can possibly cause it to synchronize the wheel speed in any curve. Each wheel's speed was controlled by two types of regulators, PI and ANN, to increase stability and reaction time (in terms of set point tracking, disturbance rejection and rise time). The proposed ANN-DTC control technique reduces torque, stator flux, and current ripple by roughly 35%. While the range of 3WES has increased by approximately 8.062 m, the battery power consumption has decreased by nearly 0.25%.</p>

In the last years electric vehicles gained importance as a more sustainable alternative to traditional vehicles. The introduction of an electric power train leads to lower air-pollution emissions. ‘Electric bicycles’ are sometimes more like an electric pedaled moped, other times more like a Vespa-looking scooter with or without pedals, and they often offer good range and speeds. However, a “scooter” can also be an electric cart for personal mobility, or a skateboard-like vehicle with small handlebars. Electric scooters are the most legislatively active realm of electric bicycles, at the present time. Brushless DC (BLDC) motors are one of the electrical drives that are rapidly gaining popularity, due to their high efficiency, good dynamic response and low maintenance. In this paper, the modeling and simulation of the BLDC motor was done using the software package MATLAB/SIMULINK. The proposed fuzzy logic controller has given optimal results compared to PI controller. The simulated system using the fuzzy controller has a fast response without overshoot, zero steady state error and high load robustness.