Performance Optimization of Fuel Cell Vehicle Air Compressor Impeller Based on Multi-objective Whale Optimization Algorithm

As fuel cell vehicles have broad development prospects, improving the performance of their air compressors is crucial for the normal and efficient operation of onboard fuel cells. To improve the performance of the air compressor and solve the contradictory relationship between the pressurization capability and efficiency of the impeller, this study proposes a method combining an approximation model and a multi-objective whale optimization algorithm. First, three-dimensional numerical models of the single-flow passage and the full compressor flow passage in the impeller were established, and the accuracy of the numerical calculations was verified using open-test-case compressors. Then, choosing the pressure ratio and isentropic efficiency as the optimization objectives, sensitivity analysis of the structural parameters of the impeller was conducted via Latin hypercube sampling, and surrogate optimization models were constructed using the least squares method for designing and setting the typical operating conditions for the impeller. Finally, the whale optimization algorithm, which is superior in impeller optimization compared to genetic algorithms, was employed to derive the Pareto solution set. Compared to the original impeller, the optimized impeller exhibited improvements in the pressure ratio as well as isentropic efficiency for the single-flow passage as well as full-model configurations. Under design conditions, the pressure ratio and isentropic efficiency increased by 5.5 % and 1.4 %, respectively, whereas under typical operating conditions, they improved by 3.6 % and 4.1 %, respectively. In addition, the three-dimensional flow characteristic analysis revealed reductions in the internal losses and recirculation within the optimized impeller. Thus, we can conclude that the proposed optimization method effectively enhances the overall performance of the fuel cell vehicle compressors.