Optimization of the operational conditions of PEMFC by a novel CFD-DT-GA approach

Proton exchange membrane fuel cells (PEMFCs) provide great efficiency and zero-pollution, making them intriguing alternatives to traditional internal combustion engines. Optimizing the global and local performance of PEMFCs under varied operational conditions remains an important issue. However, limited by the computing efficiency of the high accuracy 3D computational fluid dynamics (CFD) model, present researches focus on either the detailed multi-physics fields or wide operational condition ranges. In this paper, to address both aspects, an optimization framework, combining the CFD method, digital twin (DT) technology, and optimization model, is proposed. Operational parameters, including temperatures, relative humidity values, pressures, and stoichiometric ratios, are selected as variables to optimize PEMFC performance. The 3D CFD model is utilized to generate snapshots as the training set for DT part, in which proper orthogonal decomposition - machine learning/interpolation models are used to construct data-driven surrogate models to predict multi-physics fields. Through three-step optimization, the optimal operational conditions for the studied PEMFC are obtained: T = 68.7 °C; RH_A/C = 0/12.6 %; p_A/C = 1.7/1.5 atm; St_A/C = 1.34/3.00, resulting in a score of 98.12. This methodology could be further expanded to encompass more intricate fuel cell configurations or other energy-related applications.