Anode nitrogen content estimation and purge strategy optimization of fuel cell system

Nitrogen permeating from the cathode accumulates in the anode loop of the fuel cell system, increasing the risk of stack degradation and reducing power generation. Predicting and optimizing anode nitrogen content is crucial to improving the durability and efficiency of fuel cell systems. Conventional nitrogen prediction methods are based on the stack voltage. However, degradation has a greater impact on fuel cell stack voltage than anode nitrogen content. As a result, conventional voltage-based methods for estimating anode nitrogen content are inapplicable to a stack that is gradually degrading. In this paper, an online nitrogen content prediction model based on flow dynamics is developed and validated through experiments with an absolute error ranging from −1.5 % to +2 %. Additionally, the impact of anode nitrogen content on the energy efficiency of a fuel cell system was modeled and analyzed. Based on the anode nitrogen content estimation model and energy efficiency model, a purge strategy to control anode nitrogen content was proposed. The anode pressure drop corresponding to the optimum of nitrogen content was used as a criterion to trigger the purge valve. And the proposed purge strategy can enhance the fuel cell system energy efficiency by 0.5–1 %.