Comparison of state-of-the-art machine learning algorithms and data-driven optimization methods for mitigating nitrogen crossover in PEM fuel cells

Abstract

Nitrogen gas crossover (NGC) and nitrogen accumulation at the anode of proton exchange membrane (PEM) fuel cells are ineluctable and it would lead to inferior performance and even irreversible damage to functional components. To mitigate this issue, multiphysics numerical models (MNMs) are established to describe NGC behaviors and further guide experimental studies. However, to obtain the optimized parameters that would suppress NGC and retain high performance, grid search conducted on MSMs would cost unaffordable computational resources and time. Therefore, we innovatively introduced a machine learning-assisted MNM (MSM-ML) as a surrogate model, in which 9 state-of-the-art machine learning algorithms were compared, to greatly boost the resolution of this engineering problem. Through the proposed MSM-ML workflow performed on an experimentally validated MSM, the cost for obtaining the best parameter combination is greatly reduced. Moreover, the impact of each parameter in this complex system is directly revealed through the application of black-box interpretation methods afterwards. As a result, a new approach was pioneered to solve engineering problems which was demonstrated to be more efficient and intelligent than traditional methods. The NGC coefficient is reduced by 49.5%, while the power density is improved by 20% through the multivariable optimization of the developed MSM-ML.