PERFORMANCE EVALUATION OF A MAXIMUM POWER POINT TRACKER (MPPT) FOR SOLAR ELECTRIC VEHICLE USING ARTIFICIAL NEURAL NETWORK

Abstract

In this paper, the performance of an Artificial Neural Network (ANN) based maximum power point tracker (MPPT) for solar electric vehicles has been evaluated. The core component of a MPPT is boost converter with insulated gate bipolar transistor (IGBT) power switch. The reference voltage for MPPT is obtained by ANN with gradient descent algorithm. The tracking algorithm changes the duty-cycle of the converter so that the PV-module voltage equals the voltage corresponding to the MPPT at any given irradiance, temperature, and load conditions. For fast response, the system is implemented using digital signal processor (DSP). The overall system stability is improved by including a proportional-integralderivative (PID) or proportional-integral (PI) controller, which is also used to match the reference and battery voltage levels. The controller, based on the information supplied by the ANN, generates the boost converter duty-cycle. The energy obtained is used to charge the lithium ion battery stack for the solar vehicle. The experimental and simulation results show that the proposed scheme is highly efficient.