# Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

**Authors:** Shaik. Rafikiran, Faisal Alsaif

PMC · DOI: 10.1038/s41598-024-62208-7 · Scientific Reports · 2024-05-18

## TL;DR

This paper proposes a new hybrid controller for a DC–DC converter to improve renewable energy efficiency in automotive applications.

## Contribution

A novel hybrid MPPT controller combining fuzzy logic and grey wolf optimization is introduced for proton exchange membrane fuel stacks.

## Key findings

- The proposed controller achieves 99.421% maximum power extraction efficiency.
- The Maximum Power Point Tracking time is reduced to 0.0285 seconds.

## Abstract

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

## Full-text entities

- **Chemicals:** water (MESH:D014867)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

13 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11102481/full.md

## References

26 references — full list in the complete paper: https://tomesphere.com/paper/PMC11102481/full.md

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Source: https://tomesphere.com/paper/PMC11102481