Performance Explorations of a PMS Motor Drive Using an ANN-Based MPPT Controller for Solar-Battery Powered Electric Vehicles

Abstract

Solar energy can function as a supplementary power supply for other renewable energy sources. On average, Vellore region experiences approximately six hours of daily sunshine throughout the year. Solar photovoltaic (PV) modules are necessary to monitor and fulfill the energy requirements of a given day. An artificial neural network (ANN) based maximum power point tracking (MPPT) controller is utilised to regulate the solar photovoltaic (PV) array and enhance its output. The utilisation of this controller can enhance the efficiency of the module even in severe circumstances, where reduced current and torque ripples will be observed on the opposite end. The motorised vehicle has the capability to function at its highest torque level in different load scenarios as a result. The proposed method is expected to provide advantages in various electric vehicle (EV) applications that require consistent velocity and optimal torque to satisfy the load conditions. The study employs a solar battery that is linked to an SVPWM inverter and subsequently a DC-DC boost converter to supply power to the load. An Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) control system is proposed for a solar battery powered Electric Vehicle (EV) and the system’s performance is evaluated by collecting and analysing data under adjustable load conditions to obtain constant parameters such as speed and torque. The MATLAB® Simulink® model was utilised for this purpose.