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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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