Predict the Performance of Hydrogen Fueled Vehicle and their Refueling tation through the Data Analysis Based Approach

The widespread adoption of hydrogen-fueled vehicles (HFVs) and the deployment of Hydrogen Refueling Stations (HRS) hinge on the ability to accurately predict system performance and ensure operational reliability. This study proposes a novel predictive framework integrating mathematical modeling, state-space analysis, and advanced data mining techniques, supported by reliability analysis, to evaluate the performance of HFVs and their associated refueling infrastructure. Utilizing a public dataset of 500 real-time operational data points, key performance indicators are statistically analyzed. A significant negative correlation (r = −0.56) between hydrogen consumption and maximum vehicle range is identified, highlighting that improved hydrogen efficiency directly extends travel range. The average maximum range is 555.21 km, with a standard deviation of 87.09 km and a median of 563.65 km, indicating strong consistency across vehicles. These findings underscore the importance of optimizing fuel efficiency to enhance system sustainability and inform the design and operation of next-generation hydrogen mobility solutions. The proposed approach offers a robust foundation for performance forecasting, infrastructure planning, and policy development in hydrogen-based transportation systems.