Intelligent Estimation for Electric Vehicle Mass with Unknown Uncertainties Based on Particle Filter

Sun, S; Zhang, N; Walker, P; Lin, C

Intelligent Estimation for Electric Vehicle Mass with Unknown Uncertainties Based on Particle Filter

Sun, S

Zhang, N

Walker, P

Lin, C

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Publisher:: IET Digital Library
Publication Type:: Journal Article
Citation:: IET Intelligent Transport Systems, 2020, 14, (5), pp. 463-467
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Sun, S https://orcid.org/0000-0002-8079-8934
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044
dc.contributor.author	Walker, P https://orcid.org/0000-0003-3988-3966
dc.contributor.author	Lin, C
dc.date.accessioned	2021-02-22T01:57:59Z
dc.date.available	2021-02-22T01:57:59Z
dc.date.issued	2020
dc.identifier.citation	IET Intelligent Transport Systems, 2020, 14, (5), pp. 463-467
dc.identifier.issn	1751-9578
dc.identifier.issn	1751-9578
dc.identifier.uri	http://hdl.handle.net/10453/146279
dc.description.abstract	Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non‐linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state‐space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high‐power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.
dc.language	English
dc.publisher	IET Digital Library
dc.relation.ispartof	IET Intelligent Transport Systems
dc.relation.isbasedon	10.1049/iet-its.2019.0453
dc.rights	This paper is a postprint of a paper submitted to and accepted for publication in IET Intelligent Transport Systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Logistics & Transportation
dc.title	Intelligent Estimation for Electric Vehicle Mass with Unknown Uncertainties Based on Particle Filter
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-02-22T01:57:46Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	5

Abstract:

Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non‐linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state‐space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high‐power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.

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http://hdl.handle.net/10453/146279