New Hybrid Maximum Power Point Tracking Methods for Fuel Cell using Artificial Intelligent

TL;DR

This paper introduces two innovative AI-based MPPT methods for fuel cells, utilizing ANFIS and ICANN-trained neural networks, to optimize power extraction and reduce fuel consumption efficiently.

Contribution

It presents novel hybrid MPPT techniques combining AI models with fuzzy logic for improved fuel cell performance.

Findings

Effective power point tracking under rapid condition changes

Reduced fuel consumption compared to conventional methods

Fast and reliable operation demonstrated in simulations

Abstract

In this paper, two maximum power point tracking (MPPT) methods for Fuel Cell (FC) systems based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Imperialist Competitive Algorithm trained Neural Network (ICANN) are presented. The first operation voltage of the fuel cell corresponding to maximum power point is determined based on the data, and then the duty cycle of a DC/DC converter is adjusted using fuzzy logic controller to force the system that operates in conditions which match up with its maximum power point, in order to minimize the fuel consumption. The proposed systems and conventional fuzzy controller system are simulated in the MATLAB environment and results show acceptable operation under fast variation of conditions as well as normal conditions in minimum time.