Parameter derivation of a proton exchange membrane fuel cell based on coevolutionary ribonucleic acid genetic algorithm

Precise modeling of a polymer electrolyte membrane fuel cell (PEMFC) is a crucial issue in analyzing and controlling electrical energy production. In this paper, a novel semiexperimental model is proposed for forecasting of PEMFC output voltage. As well, the coevolution ribonucleic acid genetic algorithm (coRNA‐GA) is presented as a novel estimation approach for determination of proposed model coefficients. This optimization method is motivated by the biological RNA, encodes the chromosomes by RNA nucleotide basics, and accepts a few RNA operations. This paper proposed several genetic operators to preserve the diversity of particles, and two sets from particles are chosen using various validation functions. In these two subpopulations, different evolutionary methods have been employed for balancing of seeking and extraction. Input pressure of cathode is chosen in this paper as a further parameter for modifying the depiction of concentration overvoltage (Vcon) in the case of conventional Amphlett's PEMFC system. Finally, the performance of the coRNA‐GA algorithm, as well as the precision of the obtained model, is authenticated via empirical results. Also, the obtained results are compared with some other methods, and the superiority of the proposed model is demonstrated in voltage prediction accuracy.