Predicting fueling process on hydrogen refueling stations using multi-task machine learning.

Monitoring hydrogen refueling stations (HRS) ensures the safety of their operations as well as meeting the required performance standard for refueling. Traditionally, table-based or dynamic control of fueling process methods were used to guide the fueling process. We propose to use machine learning methods to predict the main performance target of the fueling process; the state of charge (SOC). Based on initial operating conditions in start-up fueling time, least absolute shrinkage and selection operator (LASSO) is used to predict non-dispensing fueling events, normal, and undershot dispensing events. The computational experiments were conducted on three hydrogen refueling stations with up to two-years of operational data. The classification accuracy reached over 85% on three stations. The SOC in future time steps were predicted using a multi-task regression model. The predicted future SOC within one minute is accurate on two data-rich stations. In both classification and regression tasks, the training models built on data-rich stations greatly improved the performance of a station with much less training data. These results show using machine learning methods on predicting the performance of fueling events is feasible and satisfactory on HRS. To optimize operation strategies, machine learning models can be integrated into the controller of HRS as an alternative way of controlling the fueling process. Image 1 • Machine learning methods for classification and prediction of the fueling process. • Classifying fueling events reached over 85% accuracy on three stations. • Multi-task machine learning models predicted state of charge accurately. • Models from data-rich stations improved the prediction on the data-poor station. [ABSTRACT FROM AUTHOR]