Adaptive optimization strategy of air supply for automotive polymer electrolyte membrane fuel cell in life cycle

In this study, an adaptive optimization matching method of the air supply is developed to maintain the high -efficiency operation of the automotive polymer electrolyte membrane fuel cell (PEMFC) system in the life cycle. A 1-D non-isothermal model of the PEMFC stack with 150 kW designed power and a centrifugal air compressor model are developed, considering the fuel cell performance degradation. The genetic algorithm (GA) is used to optimize the overall system efficiency under various output powers to achieve adaptive matching. The 1-D stack model is validated with the experimental test results at two states (before and after 800 h degradation), considering the effect of degradation on the matching strategies. Through the optimization method, the cen-trifugal air compressor is adaptively matched with the stack of the proposed two states to develop the compressor matching strategies under various stack conditions individually. It is found that the efficiency of the system with this optimized method is 3.8% higher than that of the system without an optimized method under the full system power range. In addition, the new matching strategy between the air compressor and the stack after degradation is exploited by the adaptive optimization method. With the help of this method, the efficiencies of the system and the stack are 5.7% and 2.9% higher than that of the matching strategy without adaptive updating. It is shown that this adaptive optimization method not only improves the output efficiency of the stack but also reduces the additional parasitic power consumed by the compressor.