Abstract
This chapter focuses on the design and analysis of a solar-fed cascaded boost converter with a maximum power point tracking (MPPT) algorithm based on neural networks (NN) for electric vehicle (EV) applications. For changes in irradiation and temperature conditions, the maximum power is extracted from the solar panel using a NN-based MPPT algorithm. The regulated output voltage is obtained from the cascaded boost converter using a proportional integral (PI) controller. The two switches in the cascaded boost converter are controlled by two different controllers. The first controller employs an NN-based MPPT algorithm to generate PWM pulses for switch 1. The second controller utilizes PI controller to produce the PWM pulse for switch 2. The simulation results show that the NN-based MPPT converter can extract the maximum power from the solar panel for changes in irradiation and temperature conditions. The output voltage is regulated by a PI controller in response to changes in irradiation, temperature, and load conditions.
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