Overall
- Language
- English
- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received July 27, 2026
Accepted August 20, 2026
Available online November 25, 2026
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This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits
unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Most Cited
Physics-Informed Hybrid Modeling for End-to-End Prediction of Hydrogen Refueling Process Using Operational Data
https://doi.org/10.1007/s11814-026-00808-x
Abstract
ial for improving refueling protocols and station operation. This study proposes a Physics-Informed Hybrid Modeling
Framework that combines thermodynamic interpretability with data-driven calibration. The framework consists of five
sub-models for supply pressure, flow rate, supply temperature, tank temperature, and tank pressure, preserving governingequation
structures while automatically calibrating 24 empirical parameters using Bayesian optimization. Approximately
32 months of operational data from a commercial hydrogen refueling station were used, with 4,943 training scenarios
for parameter calibration and 4,943 validation scenarios for Initial Value Problem (IVP)-based prediction. The framework
consistently reproduced refueling behavior across all sub-models, showing particularly high accuracy for supply pressure,
tank temperature, and tank pressure. These results demonstrate its potential as a practical simulation tool for optimizing
station operation and designing high-speed refueling strategies.
