首页|Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells

Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells

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  • NETL
  • NSTL
  • Elsevier
© 2024 The AuthorsTransfer learning (TL) is an effective method for minimizing modeling efforts and data requirements for diverse energy systems. This paper presents use of TL for different hydrogen-fueled solid oxide fuel cells (SOFCs) types (tubular vs planar) with different manufacturers (UAlberta vs Elcogen) and output power ranges. Three different single tubular cells were fabricated and tested under 18 operating conditions. In addition, a planar single cell was tested under 10 operating conditions. The data gathered from the first tubular cell (A1) was used to train a deep neural network (DNN) model with the cell voltage as the output. Then, the DNN model was transferred from this source domain to three different target domains. Two tubular cells with different cell properties such as electrolyte thickness, electrodes’ thickness and porosity, and one planar cell with different microstructural properties and different physical layout were the target domains. Fine-tuning was used for TL, and the effect of different normalization strategies and different amounts of fine-tuning data were compared. The developed DNN was able to capture the nonlinear part in current density–voltage (J–V) curves from the rich dataset available for training. The DNN model trained on cell A1 achieved a high prediction accuracy (R2 = 0.99). Training the DNN in the source domain and fine-tuning the trained network using 10% of target data results in, on average, 85% less training time. This results in a DNN model developed for the target domain, which is now as accurate as the model developed for the source domain. The applied technique reduces the computational cost by 85% and the data requirement by 90% for developing predictive models for SOFCs.

Deep learningPerformance predictionSolid oxide fuel cellSteady-state operationTransfer learning

Salehi Z.、Tofigh M.、Hanifi A.R.、Koch C.R.、Shahbakhti M.、Kharazmi A.、Smith D.J.

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Department of Mechanical Engineering

Research and Technology department (R&T)

2025

10.1016/j.ijhydene.2024.12.092

International journal of hydrogen energy

International journal of hydrogen energy

ISSN：0360-3199
年,卷(期)：2025.99(Jan.)
  • 52