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Real-time modelling and parallel optimisation of a gasoline direct injection engine

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conference contribution
posted on 2019-03-26, 15:55 authored by Wen Gu, Dezong Zhao, Byron Mason
With the increasing complexity of engines and number of control parameters, optimal engine parameter sets need to be searched in the high dimensionality. Traditional calibration methods are too complicated, expensive and timeconsuming. The model-based optimisation is of critical importance for engine fuel efficiency improvement and exhaust emissions reduction. The optimisation highly depends on the model accuracy. In this paper, a multi-layer modelling method is proposed, which can be used to generate the engine model at arbitrary operating points in real time with high accuracy. An enhanced heuristic-algorithm-based optimiser is combined with the real-time modelling method to perform a parallel optimisation. The proposed modelling and optimisation strategy can achieve the minimal fuel consumption fast and accurately. This strategy has been successfully verified using experimental data sets.

Funding

The work was cofunded by the Digital Engineering and Test Centre (DETC), under a grant for the virtually connected hybrid vehicle. DETC is a unique joint industry-academic centre, also as an Advanced Propulsion Centre spoke. It develops and uses virtual engineering tools and techniques to accelerate the development, test and manufacture of automotive propulsion systems. This work was also supported by the Engineering and Physical Sciences Research Council of U.K. under the EPSRC-UKRI Innovation Fellowship scheme (EP/S001956/1)

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

American Control Conference

Pages

5544 - 5549

Citation

GU, W., ZHAO, D. and MASON, B., 2019. Real-time modelling and parallel optimisation of a gasoline direct injection engine. Presented at the American Control Conference (ACC), Philadelphia, PA, USA, 10-12 July 2019.

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© AACC

Publisher statement

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2019-01-25

Publication date

2019-08-29

Copyright date

2019

ISBN

9781538679265

ISSN

2378-5861

Language

  • en

Location

Philadelphia, USA

Event dates

10-12 July 2019

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