Modelling and estimation parameters of electronic differential system for an electric vehicle using radial basis neural network

Abstract

This paper proposes modelling and estimation parameters of Electronic Differential System (EDS) for an Electric Vehicle (EV) with in-wheel motor using Radial Basis Neural Network (RBNN). In this study, EDS for front wheels is analysed instead of rear wheels which are commonly investigated in the literature. According to steering angle and speed of EV, the speeds of the front wheels are calculated by equations derived from Ackermann-Jeantand model using CoDeSys Software Package. The simulation of EDS is also realized by MATLAB/Simulink using the mathematical equations. Neural Network (NN) types including RBNN and Back-Propagation Feed-Forward Neural Network (BP-FFNN) are used for estimation the relationship between the steering angle and the speeds of front wheels. Besides, the different levels of noise are added to steering angle as sensor noise for realistic modelling. To conclude, the results estimated from types of NN are verified by CoDeSys and Simulink results. RBNN is convenient for estimation of EDS parameters due to robustness to different levels of sensor noise.