Model-based fault detection algorithm for liquid hydrogen refueling system using CUSUM method

Abstract

The global focus on the role of hydrogen energy in achieving carbon neutrality is increasing, particularly in transportation. Establishing and operating hydrogen refueling stations for fuel cell electric vehicles (FCEVs) are gaining prominence. This study proposes a model-based fault detection algorithm to enhance safety at large-capacity liquid hydrogen (LH2) refueling stations. First, the LH2 refueling system is modeled using Aspen HYSYS, estimating the heat transfer coefficient of the storage tank to meet the normal evaporation rate (NER) specification of 0.9 % per day. Second, diverse fault scenarios are identified via a Hazard and Operability Study (HAZOP), and simulation data are generated for the normal and fault scenarios. Finally, a fault detection algorithm utilizing the cumulative summation (CUSUM) is developed, with its threshold determined by risk levels analyzed in HAZOP. This allowed for tighter fault detection as risk levels increased. The algorithm successfully identified faults for all 11 scenarios.