Evaluating the measurement uncertainty at hydrogen refueling stations using a Bayesian non-parametric approach

Abstract

The ability to evaluate measurement error at hydrogen refueling stations plays a vital role in the sustainability of the hydrogen vehicle industry. Most previous work in this application investigates the measurement accuracy of mass flow meters in controlled experiments, using testing equipment. The focus of our work is to estimate the measurement accuracy of fueling using data from hydrogen refueling stations collected under real operation. Accuracy is estimated by comparing the observed mass count readings with reference mass counts calculated using the pressure-volume-temperature method. To quantify the measurement uncertainty, we propose using Dirichlet process mixture models, a class of Bayesian non-parametric methods. The Dirichlet process mixture model approach is tested on five hydrogen refueling stations in real operation. Our results show that the model is able to capture the complex structure of the data and successfully estimate the probability distribution of measurement uncertainty. Our work demonstrates the effectiveness of the Bayesian non-parametric approach for evaluating the measurement uncertainty of hydrogen refueling stations.