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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.
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]
Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 191453347
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PubTypeId: academicJournal
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  Data: Data-driven Modeling for Forecasting Brake System Limits in Fuel Cell Heavy-duty Trucks.
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  Data: <searchLink fieldCode="AR" term="%22Mun%2C+Seongjae%22">Mun, Seongjae</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Park%2C+Jinhui%22">Park, Jinhui</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lee%2C+Hongwoo%22">Lee, Hongwoo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ahn%2C+Changsun%22">Ahn, Changsun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sunahn@pusan.ac.kr</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Automotive+Technology%22">International Journal of Automotive Technology</searchLink>. Feb2026, Vol. 27 Issue 1, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Brake+systems%22">Brake systems</searchLink><br /><searchLink fieldCode="DE" term="%22Regenerative+braking%22">Regenerative braking</searchLink><br /><searchLink fieldCode="DE" term="%22Automotive+engineering%22">Automotive engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+transportation%22">Sustainable transportation</searchLink><br /><searchLink fieldCode="DE" term="%22Fuel+cell+vehicles%22">Fuel cell vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s12239-025-00266-0
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Brake systems
        Type: general
      – SubjectFull: Regenerative braking
        Type: general
      – SubjectFull: Automotive engineering
        Type: general
      – SubjectFull: Field research
        Type: general
      – SubjectFull: Sustainable transportation
        Type: general
      – SubjectFull: Fuel cell vehicles
        Type: general
      – SubjectFull: Statistical models
        Type: general
    Titles:
      – TitleFull: Data-driven Modeling for Forecasting Brake System Limits in Fuel Cell Heavy-duty Trucks.
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            NameFull: Mun, Seongjae
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            NameFull: Park, Jinhui
      – PersonEntity:
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            NameFull: Lee, Hongwoo
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          Name:
            NameFull: Ahn, Changsun
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            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
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              Value: 27
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            – TitleFull: International Journal of Automotive Technology
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