期刊
APPLIED ENERGY
卷 325, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119839
关键词
Polymer electrolyte membrane fuel cell; Air compressor; Genetic algorithm; Adaptive optimization; Life cycle
资金
- National Natural Science Foundation of China [52176196]
- China Postdoctoral Science Foundation [2021TQ0235]
- Hong Kong Scholars Program [XJ2021033]
- Research Grant Council, University Grants Committee, HK SAR, China [N_PolyU552/20]
An adaptive optimization matching method is developed to maintain the high-efficiency operation of automotive polymer electrolyte membrane fuel cell (PEMFC) system. The system efficiency is optimized using genetic algorithm, and matching strategies for the compressor under different stack conditions are developed. It is found that the optimized method improves system efficiency.
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.
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