Optimal parameter extraction of proton exchange membrane fuel cell using Henry gas solubility optimization

Abstract

The proton exchange membrane fuel cell (PEMFC) transforms chemical energy directly into electricity without polluting the environment or creating noise. The solid electrolyte, excellent reliability, speedy start-up, and lightweight portability distinguish PEMFC. It is difficult to predict various parameters of PEMFC optimally. Numerous metaheuristic models have been established, yet significant inaccuracy arises due to a lack of accurate estimation. So, this present study uses a new optimization approach called Henry’s gas solubility optimization (HGSO) to optimally estimate the parameters in the PEMFC model faster and more accurately. To create a balance between exploiting and inquiry in the search area and avoid local minima, the HGSO algorithm resembles gas behavior. The algorithm’s performance is assessed using ten benchmark functions, and the results are compared to five other well-known methods. Furthermore, three alternative PEMFC models are used for validation: the SR-12 modular PEMFC stack, the BCS-500 W FC stack, and the NedSstack PS-6 PEMFC stack. The results are compared to actual data. HGSO was able to provide high-quality solutions, faster convergence, shorter processing time, and less errors when compared to other algorithms studied.