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

Nan Lin, Changfu Zong and Shuming Shi
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Nan Lin: State Key Laboratory of Automotive Simulation and Control, 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

Energies, 2018, vol. 12, issue 1, 1-15

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).

Keywords: mass estimation; system error; colored noise; recursive least squares; heavy-duty vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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