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Estimation of Vehicle Dynamic Parameters Based on the Two-Stage Estimation Method.

Wenfei Li1,2,3, Huiyun Li1,2,3, Kun Xu1,2,3

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

Sensors (Basel, Switzerland)
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Summary

This study introduces a novel, two-stage method using conventional sensors to estimate crucial vehicle dynamic parameters. The approach effectively estimates parameters like centroid height and tire stiffness for improved vehicle control and modeling.

Keywords:
Unscented Kalman Filtermultiple-modelvehicle dynamic parameters

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Robotics

Background:

  • Accurate vehicle dynamic parameters are essential for developing effective active control and automated driving systems.
  • Direct measurement of many vehicle dynamics parameters is challenging and often impractical.

Purpose of the Study:

  • To propose a novel, sensor-based method for estimating key vehicle dynamic parameters.
  • To address the challenge of parameter coupling in vehicle dynamics modeling.

Main Methods:

  • A two-stage estimation approach utilizing multiple models and the Unscented Kalman Filter.
  • Stage 1: Longitudinal vehicle dynamics model for estimating centroid distance, centroid height, and longitudinal tire stiffness.
  • Stage 2: Single-track model with roll dynamics for estimating cornering stiffness and yaw/roll moments of inertia.

Main Results:

  • The proposed method successfully estimates critical vehicle dynamic parameters using only conventional sensors.
  • Simulation results validate the effectiveness of the two-stage estimation technique.
  • The method addresses parameter coupling issues inherent in vehicle dynamics.

Conclusions:

  • The developed method provides an effective means to estimate vehicle dynamic parameters crucial for advanced vehicle control.
  • This approach facilitates the creation of more accurate vehicle models for active safety and autonomous driving applications.