Multi-objective optimization of power matching for aviation SOFC-GT hybrid power system based on neural network

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Abstract
Addressing the power matching problem of the solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid power system for aviation, a multi-objective optimization approach based on neural networks was proposed. Numerical simulations were used to analyze the impact of different power distributions between the SOFC and GT on the system performance (including total system weight, thermal efficiency, electrical efficiency, fuel consumption rate, and CO2 emission rate). The BP neural network surrogate model was employed to predict the performance of various power matching schemes, and a multi-objective genetic algorithm combined with the TOPSIS decision-making method was used to optimize the power matching solution. The optimal solution was found with the SOFC output power of 133.092kW and the GT output power of 66.908kW. The optimization results show that the total system weight is 117.124 kg, the fuel consumption rate is 0.196 kg/s, and the thermal efficiency is 73.21%. Compared to a solution with equal power outputs from the SOFC and GT, the optimized solution reduces total system weight by 27.7%. This study provides a new method for optimizing power matching in aviation hybrid power systems.

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