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<p>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.</p>

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Data-driven Modeling for Forecasting Brake System Limits in Fuel Cell Heavy-duty Trucks

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Seongjae Mun,
  • Jinhui Park,
  • Hongwoo Lee,
  • Changsun Ahn

摘要

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.