Mass estimation of ground vehicles based on longitudinal dynamics using loosely coupled integrated navigation system and CAN-bus data with model parameter estimation

Kenneth M. Jensen, Ilmar F. Santos, Line K.H. Clemmensen, Søren Theodorsen, Harry G.P. Corstens

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Abstract

This work considers real-time estimation of the mass of ground vehicles equipped with on-board diagnostics (OBD-II) capabilities. The mass is estimated with a recursive least squares filter, based on vehicle longitudinal dynamics using data from a 6-axis inertial measurement unit (IMU), OBD-II data, as well as global navigation satellite system (GNSS) positions. Only basic OBD-II parameters from the controller area network (CAN-bus) are used, including vehicle speed, engine speed, and engine load. This ensures a broad applicability of the presented work. A loosely coupled fusion of IMU data, OBD-II vehicle speed, and GNSS positions is used for vehicle state estimation. The road grade is estimated from the vehicle pitch angle, current gear is predicted from the ratio of engine and vehicle speeds, and engine torque is estimated from a linear regression with engine speed and load as parameters. The regression is carried out using engine torque estimated from the vehicle longitudinal dynamics, assuming a known vehicle mass. Model parameters are estimated from the engine torque regression, utilizing a linear correlation between the engine torque and OBD-II engine load. A method for automatic estimation of vehicle gearing ratio values from drive data is also proposed. The result is a two-phase approach with an initial training phase for parameter estimation, followed by an operational phase where the vehicle mass can be estimated. The methods are validated using data from a modern car. Parameters and regression coefficients are first estimated from a single test drive. Vehicle mass is then estimated using data from 18 drives on an 85 km test route, comprising of more than 1600 km of driving under different condition, including varying vehicle loads, tire pressures, and window openings. Using the fitted model parameters, the model is generally able to estimate masses within ± 5% of the actual. The change in tire pressures and windows openings do not show significant effects on the estimated masses. Ambient wind during the test drives appears to present a significant source of uncertainty in the estimates at higher speeds. A method for compensation of the ambient wind should be investigated.
Original languageEnglish
Article number108925
JournalMechanical Systems and Signal Processing
Volume171
Number of pages26
ISSN0888-3270
DOIs
Publication statusPublished - 2022

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