Open Access

ARTICLE

Evolutionary Algorithm Based Z-Source DC-DC Boost Converter for Charging EV Battery

P. Anitha¹, K. Karthik Kumar^2,*, M. Ravindran², A. Saravanaselvan²

1 Department of Electrical and Electronics Engineering, University VOC College of Engineering, Tuticorin, 628008, India
2 Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, 628503, India

* Corresponding Author: K. Karthik Kumar. Email:

Intelligent Automation & Soft Computing 2022, 34(2), 1377-1397. https://doi.org/10.32604/iasc.2022.025396

Received 22 November 2021; Accepted 18 January 2022; Issue published 03 May 2022

Abstract

In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around (300–350) W. The battery is assumed to have an impedance model of Resistor-Capacitor (RC) load with a serial range of 12 V and 7 Ah. The proposed converter achieves higher conversion efficiency by the Maximum Power Pont Tracking (MPPT) and NSGA-II/MNSGA-II (non-dominated sorting genetic algorithm) based controller algorithm for tuning the optimal design value and is validated in a MATLAB Simulink platform. In this work, we analyze closed-loop systems under the mentioned power range. The MPPT with an algorithm-based controller tends to trigger the switch in the closed-loop system to get the optimized output. The Maximum Power Point (MPP) technique implemented is an incremental conductance method for extracting solar PV power and improving load performance. Consequently, the proposed evolutionary optimization algorithm steady-state ripple factor response of the proposed MNSGA-II has a lower output side, thus achieving around 98% of the controller implementation efficiency.

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Keywords

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