Abstract

The anode recirculation mode is increasingly being adopted in today’s fuel cell systems. The recycling of hydrogen gas can effectively improve fuel utilization and the wider economy. However, using the purge strategy for the recirculation exhaust has a significant impact on the operational performance and economic efficiency of fuel cell systems.Experiments have shown that, when the purge interval increases from 6 s to 10 s, the recirculation pump power increases by about 20%, the nitrogen content in the exhaust gas increases, and the stack voltage shows a 10 V attenuation. The accumulation of nitrogen permeation in the anode circuit leads to the degradation of the fuel cell performance. Therefore, it is necessary to discharge the accumulated nitrogen through the purge valve in a timely manner. However, opening the exhaust valve with excessively high frequency can result in the unreacted hydrogen being discharged, which reduces the economic efficiency of the fuel cell. This paper is based on the principle of mass conservation and models each subsystem of the anode circuit in the recirculation pump mode of the fuel cell separately, including the proportional valve model, the hydrogen consumption model of the fuel cell, the nitrogen permeation model of the fuel cell, the neural network model of the circulating pump, and the purge valve model. These submodels are integrated to construct a nitrogen content observer for the hydrogen circuit, which can estimate the nitrogen content. The accuracy of the model is validated through experimental data. The estimation error is less than 5.5%. The nitrogen content in the anode circuit can be effectively estimated, providing a model reference for purge operations and improving hydrogen utilization.

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