Vehicle Mass and Road Grade Estimation using Recursive Least Squares
(2016)Department of Automatic Control
- Abstract
- This Master’s thesis describes a method for real-time estimation of a vehicle’s mass for automobiles using recursive least squares. The method has its foundation in the longitudinal dynamics of the vehicle and is designed to be easy to adapt to different vehicle models.
The mass estimation rely on a underlying road grade estimation and driveline efficiency estimation for every gear. The road grade estimation is using data from the longitudinal accelerometer and is executed continuously in the vehicle. The driveline efficiency estimation is performed offline and should be a part of adaption work for each vehicle model.
The method has been validated with real measurement data in three different vehicles during different driving... (More) - This Master’s thesis describes a method for real-time estimation of a vehicle’s mass for automobiles using recursive least squares. The method has its foundation in the longitudinal dynamics of the vehicle and is designed to be easy to adapt to different vehicle models.
The mass estimation rely on a underlying road grade estimation and driveline efficiency estimation for every gear. The road grade estimation is using data from the longitudinal accelerometer and is executed continuously in the vehicle. The driveline efficiency estimation is performed offline and should be a part of adaption work for each vehicle model.
The method has been validated with real measurement data in three different vehicles during different driving situations. The robustness of the method has been investigated by simulating scenarios of badly performed offline estimations, tyre changes and for different road surfaces. The investigation shows stable results. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8884029
- author
- Paulsson, Erik and Åsman, Linnéa
- supervisor
- organization
- year
- 2016
- type
- H3 - Professional qualifications (4 Years - )
- subject
- report number
- TFRT-6009
- other publication id
- 0280-5316
- language
- English
- id
- 8884029
- date added to LUP
- 2016-08-17 09:31:38
- date last changed
- 2016-08-17 09:31:38
@misc{8884029,
abstract = {{This Master’s thesis describes a method for real-time estimation of a vehicle’s mass for automobiles using recursive least squares. The method has its foundation in the longitudinal dynamics of the vehicle and is designed to be easy to adapt to different vehicle models.
The mass estimation rely on a underlying road grade estimation and driveline efficiency estimation for every gear. The road grade estimation is using data from the longitudinal accelerometer and is executed continuously in the vehicle. The driveline efficiency estimation is performed offline and should be a part of adaption work for each vehicle model.
The method has been validated with real measurement data in three different vehicles during different driving situations. The robustness of the method has been investigated by simulating scenarios of badly performed offline estimations, tyre changes and for different road surfaces. The investigation shows stable results.}},
author = {{Paulsson, Erik and Åsman, Linnéa}},
language = {{eng}},
note = {{Student Paper}},
title = {{Vehicle Mass and Road Grade Estimation using Recursive Least Squares}},
year = {{2016}},
}