A novel intelligent-based method to control the output voltage of Proton Exchange Membrane Fuel Cell

Abstract

The Proton Exchange Membrane Fuel Cell is a low-temperature electrochemical device that offers promising advantages such as higher efficiency compared to conventional power sources, possibly a green choice with avoiding air polluting problems. The mentioned advantages will be obtained once the Fuel Cell is accurately and efficiently controlled. Experimentally, there are two significant problems in efficiently controlling of the Fuel Cell voltage including high-speed voltage oscillations and low-speed dynamic response. As well, the co-evolution ribonucleic acid genetic algorithm is presented as a novel algorithm to obtain the optimal control parameters. This algorithm is motivated from the biological Ribonucleic Acid and encodes the chromosomes by Ribonucleic Acid nucleotide basics and accepts some Ribonucleic Acid operations. Present work adopted some genetic operators to preserve the diversity of individuals, and individuals are separated into two sets. Different evolutionary methods are considered for these two sub-populations for compromising between exploration and explanation. Primarily, a comprehensive model of Proton Exchange Membrane Fuel Cell is presented. For presenting a simple application and reliable industrial control system, this paper showed a lead-lag controller. Firstly, this control system is adjusted to a certain operational point. Despite to reveal appropriate information considering the present condition of the plant, its efficiency will be decreased by varying the situations. So, the next step is employing the proposed co-evolutionary ribonucleic acid genetic algorithm for obtaining optimum values for controller parameters versus the varying conditions as well as fault occurrences. Finally, the obtained results are presented for efficiency validation of suggested control system, and these obtained results are analyzed.