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A Fusion Algorithm for Estimating Time-Independent/-Dependent Parameters and States.

Zheshuo Zhang1, Jie Zhang1, Jiawen Dai1

  • 1State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.

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Summary

This study introduces a new fusion algorithm for accurate real-time vehicle parameter estimation without needing specialized equipment. The method effectively identifies both constant and changing vehicle parameters, enhancing dynamic analysis and control systems.

Keywords:
dual unscented Kalman filtermodal analysisreal-time parameter estimationvehicle dynamicsvehicle parameter identification

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Area of Science:

  • Automotive Engineering
  • Control Systems
  • Dynamic Analysis

Background:

  • Accurate vehicle parameter estimation is crucial for dynamic analysis and control.
  • Current methods often require specialized equipment (IPMD) and only estimate partial parameters.
  • Key parameters like suspension and tire stiffnesses are frequently assumed known.

Purpose of the Study:

  • To propose a fusion algorithm for comprehensive vehicle parameter identification without an IPMD.
  • To differentiate and estimate time-independent parameters (TIPs) and time-dependent parameters (TDPs).
  • To validate the algorithm's accuracy and convergence using experimental and simulation data.

Main Methods:

  • Categorization of vehicle parameters into TIPs and TDPs.
  • Utilizing a hybrid-mass state-variable (HMSV) approach for TIP identification.
  • Employing a dual unscented Kalman filter (DUKF) for updating TDPs and online states.

Main Results:

  • Experimental validation on a two-axle vehicle demonstrated high accuracy in estimating TIPs and updating TDPs.
  • Numerical simulations confirmed robust performance under variations in sprung mass, model linearization errors, and diverse road conditions.
  • The algorithm achieved quick convergence without requiring prior road information.

Conclusions:

  • The proposed fusion algorithm effectively estimates comprehensive vehicle parameters in real-time.
  • It eliminates the need for an inertial parameter measurement device (IPMD).
  • The method offers high accuracy, rapid convergence, and adaptability to various operating conditions.