Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations

Abstract

During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of R2 when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs.

Keywords

• efficient hydrogen refueling station
• data imputation
• deep learning
• attention mechanism
• hydrogen optimization
• process optimization