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Predictive energy management of hybrid vehicle

descriptionPublicationkeyboard_double_arrow_right Conference object , Article 01 Sep 2008 France Publisher:IEEEJournal:2008 IEEE Vehicle Power and Propulsion Conference

Authors: KERMANI, Saida; DELPRAT, Sebastien; Trigui, Rochdi; Guerra, Thierry-Marie;

doi: 10.1109/vppc.2008.4677724

Predictive energy management of hybrid vehicle

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Abstract

Hybrid vehicles use two energy sources for their propelling. Usually an internal combustion engine (ICE) is used with one or more electric machine(s) (EM). The problem is then to split the driver power demand between the ICE and the EM in order to minimize a criterion, usually the fuel consumption. A global optimization algorithm based on optimal control theory is recalled. The obtained results are optimal but can only be obtained in simulation. For real time control purpose, this optimization algorithm is applied on a receding horizon. The main problem is then to choose the variables to be predicted on this horizon. By analyzing the optimization algorithm, it is shown that the prediction of the future driving conditions (vehicle speed and driver torque demand) is not necessary. Therefore, under some assumptions, a real time control is possible.

Country

France

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Keywords

PREDICTIVE CONTROL, [SPI.NRJ]Engineering Sciences [physics]/Electric power, HYBRID VEHICLES, ENERGIE, CONTROL STRATEGY, OPTIMAL CONTROL, [ SPI.NRJ ] Engineering Sciences [physics]/Electric power, ENERGY MANAGEMENT, CONTROLE, VEHICULE HYBRIDE, VEHICULE ELECTRIQUE

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	16
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average