Fleet-Based Degradation State Quantification for Industrial Water Electrolyzers

Abstract

A reliable and continuous assessment of the degradation state of industrial water electrolyzers is crucial for maintenance planning and dispatch optimization, thus facilitating risk management for both suppliers and operators. Although voltage is a widely used and easily measurable degradation indicator, its effectiveness is compromised in industrial settings due to the impact of arbitrary operating conditions. Existing methods to correct the impact of operating conditions often rely on measuring characteristic curves, which typically only provide a single-dimensional correction and do not allow varying corrections over time. We propose a data-driven method for degradation state quantification that adjusts the measured voltage under arbitrary operating conditions to a reference condition, using an empirical voltage model and degradation history from a fleet of electrolyzers. This method involves fitting the empirical voltage model for each time series segment and calculating the voltage under the reference condition. To assist model fitting under limited data coverage, the method utilizes a Bayesian approach to incorporate fleet knowledge–an aggregation of the degradation trajectories of the electrolyzer fleet. This method was validated using both synthetic data and operation data from 12 industrial electrolyzers with 1–3 years of operation history, including in-depth sensitivity analyses on the data coverage, fleet–target discrepancy, and fleet size. Results proved the superiority of the proposed fleet-based method over the benchmark method without using fleet knowledge.