Multi-Objective Operating Parameters Optimization for the Start Process of Proton Exchange Membrane Fuel Cell Stack with Non-Dominated Sorting Genetic Algorithm II

Abstract

Proton exchange membrane fuel cell (PEMFC) is one of the most promising energy conversion devices in the world. Performance, durability, and cost are the key issues currently limiting its large-scale commercial application. This paper proposes a non-dominated sorting genetic algorithm II (NSGA-II) for optimizing operating parameters of the start process to improve the output performance of a PEMFC stack. First, a Simulink model of the PEMFC stack including the anode module, cathode module, water transfer module, output voltage module, and output net power module is established, and the accuracy of the stack model is verified through experiments. The three performances are then optimized simultaneously based on NSGA-II. The results show that the optimized operating parameters for the start process results in a PEMFC stack that outperforms the base case in steady-state voltage, percentage of undershoot, and net power these three indicators with the same response time, demonstrating the success of the method in solving multiple optimization problems. This study presents an effective approach for the multi-objective optimization of the PEMFC stack, which is of guidance for engineering practice.