Online behavioral matching for proton exchange membrane water electrolyzers: A digital twin approach

The integration of proton exchange membrane water electrolyzers (PEMWEs) with renewable energy sources is pivotal for advancing sustainable hydrogen production. Digital twins (DTs) offer significant benefits by providing real-time virtual representations of physical electrolyzers without disrupting operations. However, conventional DT frameworks often rely on offline optimization to match the behavior of the DT with the physical system, which can result in mismatches during real-time operation due to data and algorithm limitations and the uncertainties of wind and solar power inputs. To address these mismatches, this study introduces a novel DT framework featuring an online behavioral matching mechanism that corrects real-time errors between the DT and physical electrolyzers. By utilizing advanced behavioral matching techniques and real-time error correction mechanisms, the proposed DT system dynamically aligns with the physical electrolyzer’s performance. Experimental validation involved a personal computer running the DT, a microcomputer with the PEMWE simulator, and a wireless cloud communication router. The results indicate that the proposed mechanism reduced errors by over 65% in the majority of cases and improved accuracy by up to 3% at most compared to traditional offline methods, suggesting that the DT can maintain more accurate real-time synchronization. While these findings are promising, further research is needed to fully assess the long-term stability and scalability of the framework in industrial applications. The enhanced DT framework shows potential to significantly improve system accuracy and reliability, providing a robust solution for real-time optimization in renewable hydrogen production. This study offers a valuable step towards more resilient and efficient cyber–physical systems, with potential applications extending beyond hydrogen production.