Estimation of vehicle mass and road slope based on steady-state Kalman filter

Shengqiang Hao; Peipei Luo; Junqiang Xi

doi:10.1109/ICUS.2017.8278412

Estimation of vehicle mass and road slope based on steady-state Kalman filter

Shengqiang Hao, Peipei Luo, Junqiang Xi^*

^*Corresponding author for this work

School of Mechanical Engineering

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Citations (Scopus)

Abstract

To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.

Original language	English
Title of host publication	Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Editors	Xin Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	582-587
Number of pages	6
ISBN (Electronic)	9781538631065
DOIs	https://doi.org/10.1109/ICUS.2017.8278412
Publication status	Published - 2 Jul 2017
Event	2017 IEEE International Conference on Unmanned Systems, ICUS 2017 - Beijing, China Duration: 27 Oct 2017 → 29 Oct 2017

Publication series

Name	Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Volume	2018-January

Conference

Conference	2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Country/Territory	China
City	Beijing
Period	27/10/17 → 29/10/17

Keywords

Acceleration sensor
Carsim/Matlab Co-simulation
Discrete Steady-state Kalman filter
Recognition of the Road slope and Vehicle Mass

Access to Document

10.1109/ICUS.2017.8278412

Cite this

Hao, S., Luo, P., & Xi, J. (2017). Estimation of vehicle mass and road slope based on steady-state Kalman filter. In X. Xu (Ed.), Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017 (pp. 582-587). (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS.2017.8278412

Hao, Shengqiang ; Luo, Peipei ; Xi, Junqiang. / Estimation of vehicle mass and road slope based on steady-state Kalman filter. Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. editor / Xin Xu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 582-587 (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017).

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title = "Estimation of vehicle mass and road slope based on steady-state Kalman filter",

abstract = "To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.",

keywords = "Acceleration sensor, Carsim/Matlab Co-simulation, Discrete Steady-state Kalman filter, Recognition of the Road slope and Vehicle Mass",

author = "Shengqiang Hao and Peipei Luo and Junqiang Xi",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Unmanned Systems, ICUS 2017 ; Conference date: 27-10-2017 Through 29-10-2017",

year = "2017",

month = jul,

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doi = "10.1109/ICUS.2017.8278412",

language = "English",

series = "Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "582--587",

editor = "Xin Xu",

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}

Hao, S, Luo, P & Xi, J 2017, Estimation of vehicle mass and road slope based on steady-state Kalman filter. in X Xu (ed.), Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 582-587, 2017 IEEE International Conference on Unmanned Systems, ICUS 2017, Beijing, China, 27/10/17. https://doi.org/10.1109/ICUS.2017.8278412

Estimation of vehicle mass and road slope based on steady-state Kalman filter. / Hao, Shengqiang; Luo, Peipei; Xi, Junqiang.
Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. ed. / Xin Xu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 582-587 (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.

AB - To solve the problem that control system of the intelligent vehicle is hard to measure the vehicle mass and road gradient, this paper built a longitudinal dynamics model of vehicle. Based on theoretical model, discrete steady-state Kalman filter was used to estimate gradient of slope and vehicle mass, and simulation platform was established by Carsim and Maltab/Simulink to verify the accuracy and instantaneity of the algorithm. A proper acceleration sensor was selected, according to the stable Kalman filter theory. A real test was conducted, and the instantaneity and accuracy of this method for vehicle mass and road slope was verified by comparing with the data from inertial navigator.

KW - Acceleration sensor

KW - Carsim/Matlab Co-simulation

KW - Discrete Steady-state Kalman filter

KW - Recognition of the Road slope and Vehicle Mass

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Hao S, Luo P, Xi J. Estimation of vehicle mass and road slope based on steady-state Kalman filter. In Xu X, editor, Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 582-587. (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017). doi: 10.1109/ICUS.2017.8278412

Estimation of vehicle mass and road slope based on steady-state Kalman filter

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