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Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car

Research output: Contribution to journalArticle

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Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car. / Wragge-Morley, Robert T; Herrmann, Guido; Barber, P; Burgess, Stuart C.

In: SAE International Journal of Passenger Cars - Mechanical Systems, Vol. 8, No. 1, 14.04.2015, p. 137-145.

Research output: Contribution to journalArticle

Harvard

Wragge-Morley, RT, Herrmann, G, Barber, P & Burgess, SC 2015, 'Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car', SAE International Journal of Passenger Cars - Mechanical Systems, vol. 8, no. 1, pp. 137-145. https://doi.org/10.4271/2015-01-0201

APA

Wragge-Morley, R. T., Herrmann, G., Barber, P., & Burgess, S. C. (2015). Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car. SAE International Journal of Passenger Cars - Mechanical Systems, 8(1), 137-145. https://doi.org/10.4271/2015-01-0201

Vancouver

Wragge-Morley RT, Herrmann G, Barber P, Burgess SC. Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car. SAE International Journal of Passenger Cars - Mechanical Systems. 2015 Apr 14;8(1):137-145. https://doi.org/10.4271/2015-01-0201

Author

Wragge-Morley, Robert T ; Herrmann, Guido ; Barber, P ; Burgess, Stuart C. / Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car. In: SAE International Journal of Passenger Cars - Mechanical Systems. 2015 ; Vol. 8, No. 1. pp. 137-145.

Bibtex

@article{849e8838d3da465c9d3748cbc9e71376,
title = "Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car",
abstract = "We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS). Therefore, a method using some filtering and supervisory logic is employed to give a sensible update behaviour for the vehicle mass estimation algorithm. The main function of the supervisor is to reject the mass estimate produced by unsuitable available data due to unmodelled loss forces. Good estimation results are obtained from data from a vehicle which was also fitted with some additional instrumentation including GPS sensors and a high quality IMU for scientific verification purposes.",
author = "Wragge-Morley, {Robert T} and Guido Herrmann and P Barber and Burgess, {Stuart C}",
note = "Date of Acceptance: 01/04/2015 (originally submitted to a peer reviewed conference of the SAE: SAE 2015 World Congress & Exhibition 21/04/2015-23/04/2015)",
year = "2015",
month = "4",
day = "14",
doi = "10.4271/2015-01-0201",
language = "English",
volume = "8",
pages = "137--145",
journal = "SAE International Journal of Passenger Cars - Mechanical Systems",
issn = "1946-3995",
publisher = "SAE International",
number = "1",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Gradient and Mass Estimation from CAN Based Data for a Light Passenger Car

AU - Wragge-Morley, Robert T

AU - Herrmann, Guido

AU - Barber, P

AU - Burgess, Stuart C

N1 - Date of Acceptance: 01/04/2015 (originally submitted to a peer reviewed conference of the SAE: SAE 2015 World Congress & Exhibition 21/04/2015-23/04/2015)

PY - 2015/4/14

Y1 - 2015/4/14

N2 - We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS). Therefore, a method using some filtering and supervisory logic is employed to give a sensible update behaviour for the vehicle mass estimation algorithm. The main function of the supervisor is to reject the mass estimate produced by unsuitable available data due to unmodelled loss forces. Good estimation results are obtained from data from a vehicle which was also fitted with some additional instrumentation including GPS sensors and a high quality IMU for scientific verification purposes.

AB - We present a method for the estimation of vehicle mass and road gradient for a light passenger vehicle. The estimation method uses information normally available on the vehicle CAN bus without the addition of extra sensors. A composite parameter estimation algorithm incorporating a nonlinear adaptive observer structure uses vehicle speed over ground and driving torque to estimate mass and road gradient. A system of filters is used to avoid deriving acceleration directly from wheel speed. In addition, a novel data fusion method makes use of the regressor structure to introduce information from other sensors in the vehicle. The dynamics of the additional sensors must be able to be parameterised using the same parameterisation as the complete vehicle system dynamics. In this case we make use of an Inertial Measurement Unit (IMU) which is part of the vehicle safety and Advanced Driver Assist Systems (ADAS). Therefore, a method using some filtering and supervisory logic is employed to give a sensible update behaviour for the vehicle mass estimation algorithm. The main function of the supervisor is to reject the mass estimate produced by unsuitable available data due to unmodelled loss forces. Good estimation results are obtained from data from a vehicle which was also fitted with some additional instrumentation including GPS sensors and a high quality IMU for scientific verification purposes.

U2 - 10.4271/2015-01-0201

DO - 10.4271/2015-01-0201

M3 - Article

VL - 8

SP - 137

EP - 145

JO - SAE International Journal of Passenger Cars - Mechanical Systems

JF - SAE International Journal of Passenger Cars - Mechanical Systems

SN - 1946-3995

IS - 1

ER -