Hierarchical evolutionary modeling and performance multi-objective optimization of centrifugal air compressors for fuel cells under multi-operating conditions

Abstract

The air compressor is responsible for supplying high-pressure air in fuel cell system, which is essential for supporting the efficiency and stability of the electrochemical reaction. In this study, a multi-physics simulation model of a compressor was constructed using comprehensive performance test data, and hierarchical evolutionary models were developed based on an integrated simulation framework. On this basis, multi-objective performance optimization of compressor was conducted under multiple operating conditions. The results demonstrate that the multi-physics model accurately simulates the complex flow process within the compressor, with an error of less than 3% compared to test data. The eXtreme Gradient Boosting and Hybrid Variational Artificial Bee Colony (XGBoost+HyVABC) model exhibits strong generalization ability and predictive accuracy, with an error of less than 3% on test set. The maximum total improvement percentage non-dominated solution achieves a comprehensive performance improvement for the compressor under various operating conditions, with isentropic efficiency at 5000 r/min, pressure ratio at 7000 r/min and isentropic efficiency at 11000 r/min improved by 23.4%, 17.6% and 2.5%, respectively. Simulation verification confirms that simulation results of non-dominated solutions maintain the non-dominated characteristics observed in predicted results, with errors between simulated and predicted values not exceeding 2%. These findings provide methodological reference and data foundation for development of high-performance air compressors.