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The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics

Author

Listed:
  • Nan Lin

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Jilin 130022, China
    Transportation College, Jilin University, Jilin 130022, China)

  • Changfu Zong

    (State Key Laboratory of Automotive Simulation and Control, Jilin University, Jilin 130022, China)

  • Shuming Shi

    (Transportation College, Jilin University, Jilin 130022, China)

Abstract

Vehicle mass is a critical parameter for economic cruise control. With the development of active control, vehicle mass estimation in real-time situations is becoming notably important. Normal state estimators regard system error as white noise, but many sources of error, such as the accuracy of measured parameters, environment and vehicle motion state, cause system error to become colored noise. This paper presents a mass estimation method that considers system error as colored noise. The system error is considered an unknown parameter that must be estimated. The recursive least squares algorithm with two unknown parameters is used to estimate both vehicle mass and system error. The system error of longitudinal dynamics is analyzed in both qualitative and quantitative aspects. The road tests indicate that the percentage of mass error is 16%, and, if the system error is considered, the percentage of mass error is 7.2%. The precision of mass estimation improves by 8.8%. The accuracy and stability of mass estimation obviously improves with the consideration of system error. The proposed model can offer online mass estimation for intelligent vehicle, especially for heavy-duty vehicle (HDV).

Suggested Citation

  • Nan Lin & Changfu Zong & Shuming Shi, 2018. "The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics," Energies, MDPI, vol. 12(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:52-:d:193050
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    References listed on IDEAS

    as
    1. Nan Lin & Changfu Zong & Shuming Shi, 2018. "Method for Switching between Traction and Brake Control for Speed Profile Optimization in Mountainous Situations," Energies, MDPI, vol. 11(11), pages 1-13, November.
    2. Bae, Hong S. & Gerdes, J. Christian, 2003. "Parameter Estimation and Command Modification for Longitudinal Control of Heavy Vehicles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6s35h1ch, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Jiawei Guo & Chao He & Jiaqiang Li & Heng Wei, 2022. "Slope Estimation Method of Electric Vehicles Based on Improved Sage–Husa Adaptive Kalman Filter," Energies, MDPI, vol. 15(11), pages 1-17, June.

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