Reliably predicting the service life of electrolysers under real-world, dynamic operating conditions is a key challenge for the economically viable production of green hydrogen. While traditional accelerated stress tests (ASTs) provide degradation data quickly, they are only of limited use for predicting long-term degradation under varying real-world operating conditions.
As part of the pAST-EL project, ZBT and DLR are therefore developing a new type of predictive accelerated stress test (pAST). The test protocols are specifically designed to generate a particularly meaningful data set for service life prediction models within the shortest possible test times.
The approach is being experimentally validated using alkaline electrolysis (AEL). To this end, pAST-based cell tests and long-term measurements are being conducted under realistic dynamic operating conditions. Machine-learning-based lifespan models are developed and validated using the measurement data. Building on this, critical operating conditions will be identified and lifespan-optimised operating strategies derived. Subsequently, their impact on degradation, hydrogen production costs, and greenhouse gas intensity will be investigated.
The methodology developed is intended to be applicable across various technologies. Protocol development, lifetime prediction, and corresponding software tools are therefore provided as open, user-customisable methods.
