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Microstructure-Informed Performance Boost in Solid Oxide Fuel Cells through Multiphysical Modeling and Machine Learning
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DOI：10.1039/D5TA03421C.png)
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Abstract
En 中文
The optimization of the macro- and microstructures of traditional solid oxide fuel cells (SOFCs) faces the dual challenges of time-consuming experimental iterations and insufficient exploration of parameter space. This study proposes an anode-supported SOFC optimization approach based on multiphysics modeling and machine learning; aiming to achieve the coordinated optimization design of its macro- and microstructures; thereby ensuring the improvement of power density and the reduction of failure probability. The study first constructed a database of maximum power density and failure probability based on multiphysics modeling; and then screened out 10 key features that affect the above two target parameters through feature engineering. On this basis; 15 machine learning predictive models were constructed; among which the random forest (RF) regression model showed excellent prediction performance; and the determination coefficients (R²) of the maximum power density and failure probability predictive models reached 0.99 and 0.95 respectively. The cooperation of genetic algorithm and RF obtained the optimal combination of key parameters; ensuring that the cell achieved the highest power output within the failure probability range of 0.632. The SOFC button cell prepared based on the cathode optimization results was experimentally verified; and its maximum power density reached 1.43 W/cm2; which was 29% higher than the initial sample; verifying the effectiveness of the proposed optimization approach. In addition; Shapley additive explanations (SHAP) were introduced to improve the interpretability of the model. The results show that most key features have opposite effects on the two target quantities; demonstrating the necessity of considering the failure probability.
Keywords:
SOFC optimization
multiphysics modeling
machine learning
power density
failure probability
Journal
IF:
9.5
Papers:
3.3W
Citations:
21.7W
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