Efficient Control of Sensorless Hybrid Electric Vehicle Using RBFN Controller

Abstract

In this paper, a Radial Basis Function Network (RBFN) controlled Sensorless Hybrid Electric Vehicle (HEV) using a Brushless DC Motor (BLDC) is presented. In this context, hybrid energy sources (Photovoltaic& Fuel cell) are designed for supplying electrical energy to the HEV. This involves the design of a Maximum Power Point Tracking (MPPT) system using a Fuzzy Logic controller (FLC) in order to track the maximum power obtained from hybrid energy sources under various weather conditions. The speed of HEV drives is efficiently controlled by using the RBFN-ANN controller in comparison with sensor based control techniques. The performance of the proposed HEV was tested for two types of controllers, namely the Proportional Integral (PI) and the RBFN-ANN controller. The performance of the system is analysed with respect to the three different load torque conditions. The performance comparison is made in terms of generated torque ripple and speed response. The proposed hybrid electric vehicle using a RBFN-ANN controller is validated through the MATLAB/Simulink environment.

ANN Optimized Hybrid Energy Management Control System for Electric Vehicles

“…The demand for sustainable energy sources has been increasing drastically for the last few years due to their remarkable benefits. The field of the automobile industry also shows a huge interest in the utilization of sustainable energy sources which leads to a reduction in usage of fuel resulting in a huge reduction in carbon emissions from the conventional electric vehicle (Ravipati et al, 2021). Solar Photovoltaic (PV) energy has been chosen as a source among the several sustainable energy sources due to its huge availability (Gulagi et al, 2020).…”