Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/126051
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Type: Conference paper
Title: Predictive modeling for energy control in hybrid electric vehicle systems
Author: Shen, D.
Lim, C.C.
Shi, P.
Citation: Proceedings / International Conference on Machine Learning and Cybernetics. International Conference on Machine Learning and Cybernetics, 2019, vol.2019-July, pp.12-18
Publisher: IEEE
Issue Date: 2019
Series/Report no.: International Conference on Machine Learning and Cybernetics
ISBN: 9781728128160
ISSN: 2160-133X
2160-1348
Conference Name: International Conference on Machine Learning and Cybernetics (ICMLC) (7 Jul 2019 - 10 Jul 2019 : Kobe, Japan)
Statement of
Responsibility: 
Di Shen, Cheng-Chew Lim, Peng Shi
Abstract: In this paper, the predictive and optimal control problem of energy consumption is investigated for hybrid vehicle systems. In order to make the energy usage more efficient (minimizing the energy demand), observations of drivers’ behavior are taken into consideration in analysis and predictive control design. Real world driving experiments are conducted to illustrate the effective and accuracy of the proposed new design techniques.
Keywords: Driver behaviour; stochastic model predictive control; hybrid electric vehicle; predictive state representation
Rights: Copyright © 2019 by the Institute of Electrical and Electronics Engineers All rights reserved.
DOI: 10.1109/icmlc48188.2019.8949301
Grant ID: http://purl.org/au-research/grants/arc/DP170102644
Published version: http://dx.doi.org/10.1109/icmlc48188.2019.8949301
Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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