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<p>Accurate estimation of a truck’s mass and center of gravity (CG) is critical for optimizing safety and performance but remains challenging due to dynamic uncertainties in weight distribution and road interactions. This study introduces a data-driven mechanics framework integrating four hybrid machine learning (ML) models-tuna search-optimized support vector machine, cuckoo search-optimized BP neural networks, sparrow search algorithm-optimized extreme learning machine, and whale search-optimized XGBoost-to enable estimation. A 17-degree-of-freedom multibody dynamics model, incorporating suspension kinematics via a semirecursive formulation, generates simulation datasets linking real-time tuck states (pitch, roll) to mass and CG. Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters, enhancing training efficiency. Validation against multibody benchmarks confirms accuracy, while robustness is demonstrated across driving scenarios and noise. By unifying data-driven ML with physics-based mechanics, this approach advances parameter estimation, bridging truck dynamics with computational intelligence for automotive design.</p>

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Estimation of truck mass and center of gravity using data-driven mechanics: a comparative study of TSO-SVM, CS-BP, SSA-ELM, and WOA-XGBoost in automotive dynamics

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Wei Liu,
  • Yuanyou Fu,
  • Yongjun Pan,
  • Weicheng Huang,
  • Aki Mikkola

摘要

Accurate estimation of a truck’s mass and center of gravity (CG) is critical for optimizing safety and performance but remains challenging due to dynamic uncertainties in weight distribution and road interactions. This study introduces a data-driven mechanics framework integrating four hybrid machine learning (ML) models-tuna search-optimized support vector machine, cuckoo search-optimized BP neural networks, sparrow search algorithm-optimized extreme learning machine, and whale search-optimized XGBoost-to enable estimation. A 17-degree-of-freedom multibody dynamics model, incorporating suspension kinematics via a semirecursive formulation, generates simulation datasets linking real-time tuck states (pitch, roll) to mass and CG. Search algorithms leverage physics-derived truck state data to initialize ML hyperparameters, enhancing training efficiency. Validation against multibody benchmarks confirms accuracy, while robustness is demonstrated across driving scenarios and noise. By unifying data-driven ML with physics-based mechanics, this approach advances parameter estimation, bridging truck dynamics with computational intelligence for automotive design.