Computation of the Speed of Four In-Wheel Motors of an Electric Vehicle Using a Radial Basis Neural Network

Abstract

This paper presents design and speed estimation for an Electric Vehicle (EV) with four in-wheel motors using Radial Basis Neural Network (RBNN). According to the steering angle and the speed of EV, the speeds of all wheels are calculated by equations derived from the Ackermann-Jeantand model using CoDeSys Software Package. The Electronic Differential System (EDS) is also simulated by Matlab/Simulink using the mathematical equations. RBNN is used for the estimation of the wheel speeds based on the steering angle and EV speed. Further, different levels of noise are added to the steering angle and the EV speed. The speeds of front wheels calculated by CoDeSys are sent to two Induction Motor (IM) drives via a Controller Area Network-Bus (CAN-Bus). These speed values are measured experimentally by a tachometer changing the steering angle and EV speed. RBNN results are verified by CoDeSys, Simulink, and experimental results. As a result, it is observed that RBNN is a good estimator for EDS of an EV with in-wheel motor due to its robustness to different levels of sensor noise.