Modeling of Proton Exchange Membrane Fuel Cell Stack and Start Strategy Optimization with a Multi-Objective Genetic Algorithm

Abstract

This paper proposes a non-dominated ranking genetic algorithm (NSGA-II) method for fast optimizing the loading strategy at the start of a proton exchange membrane fuel cell (PEMFC) stack to optimize the dynamic response capability, power density and system efficiency. First, a Simulink model of the PEMFC stack including anode module, cathode module, water transfer module and output voltage module is established as the base model for optimization, and the accuracy of the stack model is verified through experiments. The power consumption of the air compressor is the main parasitic power considered, and it is modelled using the air compressor map by taking a table look-up method directly. The three performance indicators of dynamic response capability, power density and system efficiency are then optimized simultaneously based on NSGA-II. The results show that the optimized start-up loading strategy results in a PEMFC stack that outperforms the base model in all three indicators, demonstrating the success of the method in solving time-consuming multiple optimization problems. This study presents an effective and rapid approach for the multi-objective optimization of PEMFC stack, which is of guidance for engineering practice.