Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Abstract

For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

Parameters Estimation of Proton Exchange Membrane Fuel Cell Model Based on an Improved Walrus Optimization Algorithm

“…Various modeling approaches are employed [21], [22], encompassing analytical, empirical, semi-empirical, and theoretical methods to simulate PEMFC performance [23]. Theoretical extraction of PEMFC parameters involves both conventional and metaheuristic methods [24]. Reference [25] focuses on PEMFC parameter estimation, highlighting the challenges posed by nonlinear systems for accurate modeling.…”

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

“…Finetuning of artificial intelligence. Hyper-parameter optimization is essential to the development of efficient models in machine learning and deep learning algorithms, as well as for quality control in industrial production 100,101 . JSO is an efficient and innovative algorithm that is used in hyper-parameter optimization.…”

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance

“…Due to their adaptability, they can deal with ambiguities in problem formulation and complex challenges. MAs can be grouped into: Swarm Intelligence Algorithms (SI), Evolutionary-Based Algorithms(EB), and Physics-Based Algorithms (PB) 18 . Mathematics or Physics algorithms are created based on mathematical and physical natural principles.…”