Power and Cost Optimization Design of PEM Fuel Cell System using a Hierarchical Multi-Objective Optimization Algorithm

Enhancing the performance metrics of polymer electrolyte membrane (PEM) fuel cells, including net power and hydrogen cost, is critically important, especially considering parameter uncertainty. This study introduces an advanced adaptive multi-objective optimization framework. Initially, a steady-state nonlinear model is developed as the foundational model for the PEM fuel cell system. Subsequently, the multi-objective particle swarm optimization (MPSO) algorithm with parameter adjusting is employed during the optimization phase to achieve precise operational parameter optimization. To balance convergence and the diversity of Pareto solutions, an adaptive flight parameter strategy is implemented. Additionally, to mitigate the impact of parameter uncertainty, an interval optimization method is applied in the online optimization layer based on MOPSO results. Different comparative simulation results are implemented and the superiority of proposed hierarchical optimization scheme.