Developing a Modular Uncertainty Calculation Tool for Hydrogen Refueling Stations
Master thesis

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Date
2024-06-03Metadata
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- Master theses [179]
Abstract
The rapidly expanding hydrogen industry has highlighted the need for reliable metrology to ensure accurate fiscal measurements throughout its supply chain. Despite advancements, significant measurement challenges have been reported to cause billing errors in existing Hydrogen Refueling Stations (HRS). This thesis addresses these challenges by developing an uncertainty model based on an analysis of the HRS system and relevant literature, specified towards fiscal gas measurements with a Coriolis Flow Meter (CFM). Based on this model, a modular Python-based calculation framework was developed to enhance the precision of fiscal measurements and ensure compliance with standards such as OIML R-139 and SAE J2601. The framework was utilized to analyze and quantify the impacts of systematic mass errors and the uncertainties arising from their correction. Analysis revealed the critical impact systematic errors have on the system if left unaddressed, underscoring the need for correction. The framework was also used to calculate the uncertainties associated with two simulated filling protocols, set at the Minimum Measurable Quantity(MMQ) of 1 kg and a full 5 kg filling. Analysis revealed that post-correction, the CFM was the primary source of uncertainty, highlighting the need for proper calibration methods and protocols. Additionally, the analysis showed that, with corrections applied, the uncertainty associated with a Configuration 1 HRS is lower than that of a Configuration 2 HRS. This study provides valuable insights into the HRS measurement system, contributing to a field where information is currently limited.