The transition to low-carbon energy systems is essential to mitigating climate change, with green hydrogen emerging as a key enabler for decarbonising hard-to-electrify sectors. Proton Exchange Membrane (PEM) electrolysers, valued for their fast dynamic response to renewable energy variability, face significant degradation challenges under intermittent operation. However, the absence of standardised testing protocols, differences in experimental methodologies, input signals, test benches and Accelerated Stress Testing (AST) procedures have resulted in widely inconsistent degradation rates (up to four orders of magnitude), hindering cross-study comparison and industrial scalability. This study introduces a novel Dynamism metric aimed at harmonising experimental methodologies by classifying the loading profiles applied to PEM electrolysers. The proposed metric integrates five key parameters ( i.e. mean load, load range, cycling period, and ramp-up and ramp-down rates) derived through a systematic procedure based on rainflow counting techniques. The technique can be used with diverse loading signals, including realistic solar and wind profiles, as well as simplified square and triangular waves. By applying this framework to an extensive experimental dataset, the study reveals a quadratic relationship between signal Dynamism and degradation rate, indicating that abrupt, high-frequency fluctuations significantly accelerate performance loss in PEM electrolysers. Finally, the methodology is validated through two independent dataset. These findings highlight the need for standardised testing protocols to isolate degradation mechanisms, validate durability claims and optimise PEM electrolyser design for renewable energy integration. Furthermore, the code implemented in Python and Matlab for the benchmarking has been released as open source and are available at https://github.com/MGEP-TEFLU/PEMdegradation-Dynamism4AST . • Introduces a systematic benchmarking framework for PEM electrolyser degradation. • Proposes a novel Dynamism metric for experimental design and real-world result transfer. • Demonstrates a quadratic relationship between signal Dynamism and degradation rate. • Supports standardisation and comparability of degradation studies across literature.
Aizpuru et al. (2026) studied this question.