Seoul National University of Science and Technology Researchers Develop AI Framework for Optimizing Solid Oxide Electrolysis Cells

As countries invest in green hydrogen to decarbonize heavy industry, transportation, and energy systems, researchers are seeking faster ways to accelerate the development of hydrogen technologies. Solid oxide electrolysis cells are among the most efficient systems for producing green hydrogen from steam, but identifying their optimal operating conditions requires thousands of computationally intensive simulations, slowing innovation and increasing development costs.

Researchers from Korea have now developed an artificial intelligence (AI)-guided optimization framework that dramatically reduces the computational effort needed to optimize solid oxide electrolysis cell (SOEC) operation. By combining high-fidelity computational fluid dynamics (CFD) simulations with an AI-driven active learning framework, the researchers rapidly identified operating conditions that improve hydrogen production efficiency while maintaining the thermal stability required for long-term operation. This paper was made available online on 25 June 2026 and has been published in Volume 303, Part 1, of the journal Applied Thermal Engineering on August 1 2026.

Instead of evaluating every possible operating condition, the AI framework learns from each completed simulation and predicts which operating conditions are most likely to provide valuable new information. This allows researchers to focus computational resources on the most promising operating regions, replacing exhaustive trial-and-error searches with a faster, more data-efficient optimization strategy.

Unlike conventional approaches that seek a single optimum, the framework identifies a Pareto-optimal operating region that balances two competing objectives: maximizing electrochemical performance while minimizing temperature differences that can accelerate material degradation and reduce device lifespan. This gives engineers the flexibility to select operating conditions based on practical priorities, whether maximizing efficiency, improving durability, or achieving the best compromise between the two.

The framework improved the electrochemical performance index (EPI) by 14% while reducing in-plane temperature differences by 80% compared with the baseline operating condition. Compared with conventional random sampling using the same computational budget, the AI-guided approach achieved 2.5% higher final EPI and a 90.5% lower final temperature difference.

Most significantly, the framework achieved these results using only 17 high-fidelity CFD simulations. An exhaustive search across the same operating space would have required 6,561 simulations, equivalent to approximately 22,963.5 computational hours. The AI-guided framework achieved comparable optimization performance in just 60 hours, demonstrating its potential to shorten engineering design cycles and accelerate the development and commercialization of efficient green hydrogen technologies.

"Optimizing advanced hydrogen technologies has traditionally required enormous computational resources because engineers often need to evaluate thousands of possible operating conditions," said Mingi Choi, Assistant Professor in the Department of Future Energy Convergence at Seoul National University of Science and Technology. "By learning which simulations are most informative, our framework dramatically reduces the computational effort needed to find promising operating conditions. We believe this approach can accelerate the development of green hydrogen technologies and support faster innovation across a wide range of engineering applications."

Beyond SOECs, the researchers believe the AI-guided framework could accelerate the design of fuel cells, batteries, catalytic systems, and other energy technologies that rely on computationally expensive simulations. By reducing computational cost while maintaining optimization quality, the AI-guided framework could increasingly support scientific discovery by shortening development cycles and helping bring clean-energy technologies to market more quickly.

Reference

Title of original paper: Multi-objective optimization of solid oxide electrolysis cell efficiency and thermal gradient using an active-learning–CFD hybrid framework
Journal: Applied Thermal Engineering
DOI