Data-driven Modeling for Forecasting Brake System Limits in Fuel Cell Heavy-duty Trucks.
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| Title: | Data-driven Modeling for Forecasting Brake System Limits in Fuel Cell Heavy-duty Trucks. |
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| Authors: | Mun, Seongjae1 (AUTHOR), Park, Jinhui2 (AUTHOR), Lee, Hongwoo2 (AUTHOR), Ahn, Changsun1 (AUTHOR) sunahn@pusan.ac.kr |
| Source: | International Journal of Automotive Technology. Feb2026, Vol. 27 Issue 1, p1-12. 12p. |
| Subjects: | Forecasting, Brake systems, Regenerative braking, Automotive engineering, Field research, Sustainable transportation, Fuel cell vehicles, Statistical models |
| Abstract: | This study introduces a modeling approach to forecast brake operational limits in fuel cell heavy-duty trucks using field test data. Traditional braking system evaluation, which relies on time-consuming and costly field and dynamometer tests, is challenged by the complexity of fuel cell heavy-duty trucks, particularly the interaction between the braking and energy systems facilitated by regenerative braking. By utilizing data-driven models, this research significantly reduces the complexity of modeling these systems. The developed model, validated against real-world downhill driving scenarios, demonstrates an exceptional accuracy rate of approximately 99% in forecasting brake limit points. This method not only offers a highly reliable tool for forecasting brake operational limits but also helps optimize vehicle design and efficiency, contributing to the advancement of sustainable transportation solutions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | This study introduces a modeling approach to forecast brake operational limits in fuel cell heavy-duty trucks using field test data. Traditional braking system evaluation, which relies on time-consuming and costly field and dynamometer tests, is challenged by the complexity of fuel cell heavy-duty trucks, particularly the interaction between the braking and energy systems facilitated by regenerative braking. By utilizing data-driven models, this research significantly reduces the complexity of modeling these systems. The developed model, validated against real-world downhill driving scenarios, demonstrates an exceptional accuracy rate of approximately 99% in forecasting brake limit points. This method not only offers a highly reliable tool for forecasting brake operational limits but also helps optimize vehicle design and efficiency, contributing to the advancement of sustainable transportation solutions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 12299138 |
| DOI: | 10.1007/s12239-025-00266-0 |
