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

Lin, Nan ; Zong, Changfu ; Shi, Shuming

Energies (Basel), 2019-01, Vol.12 (1), p.52 [Periódico revisado por pares]

Basel: MDPI AG

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  • Título:
    The Method of Mass Estimation Considering System Error in Vehicle Longitudinal Dynamics
  • Autor: Lin, Nan ; Zong, Changfu ; Shi, Shuming
  • Assuntos: Acceleration ; Accuracy ; Algorithms ; colored noise ; Controller area network ; heavy-duty vehicle ; Kalman filters ; Laboratories ; mass estimation ; Noise ; Parameter estimation ; recursive least squares ; system error ; Test vehicles ; Variables
  • É parte de: Energies (Basel), 2019-01, Vol.12 (1), p.52
  • Descrição: 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).
  • Editor: Basel: MDPI AG
  • Idioma: Inglês
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