State-of-Health Estimation for Industrial <i>H</i>₂ Electrolyzers with Transfer Linear Regression

Abstract

Water electrolysis to generate green hydrogen is the key to decarbonization. Tracking the state-of-health of electrolyzers is fundamental to ensuring their economical and safe operation. This paper addresses the challenge of quantifying the state-of-health of electrolyzers, which is complicated by the influence of operating conditions. The existing approaches require stringent control of operating conditions, such as following a predefined current profile and maintaining a constant temperature, which is impractical for industrial applications. We propose a data-driven method that corrects the measured voltage under arbitrary operating conditions to a reference condition, serving as a state-of-health indicator for electrolyzers. The method involves fitting a voltage model to map the relationship between voltage and operating conditions and then using this model to calculate the voltage under predefined reference conditions. Our approach utilizes an empirical voltage model, validated with actual industrial electrolyzer operation data. We further introduce a transfer linear regression algorithm to tackle model fitting difficulties with limited data coverage. Validation on synthetic data confirms the algorithm’s effectiveness in capturing the true model coefficients, and application on actual operation data demonstrates its ability to provide stable state-of-health estimations. This research offers a practical solution for the industry to continuously monitor electrolyzer degradation without the need for stringent control of operating conditions.

Keywords

• hydrogen
• electrolyzer
• degradation
• condition monitoring
• state-of-health
• transfer learning