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Real time energy management of electrically turbocharged engines based on model learning

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conference contribution
posted on 2019-03-26, 15:03 authored by Dezong Zhao, Wen Gu, Byron Mason
Engine downsizing is a promising trend to decarbonise vehicles but it also poses a challenge on vehicle driveability. Electric turbochargers can solve the dilemma between engine downsizing and vehicle driveability. Using the electric turbocharger, the transient response at low engine speeds can be recovered by air boosting assistance. Meanwhile, the introduction of electric machine makes the engine control more complicated. One emerging issue is to harness the augmented engine air system in a systematical way. Therefore, the boosting requirement can be achieved fast without violating exhaust emission standards. Another raised issue is to design an real time energy management strategy. This is of critical to minimise the required battery capacity. Moreover, using the on-board battery in a high efficient way is essential to avoid over-frequent switching of the electric machine. This requests the electric machine to work as a generator to recharge the battery. The capability of generating power strongly depends on the engine operating point. One big challenge is that the calibration of generating power capability is time-consuming in experiments. This paper proposes a neuro-fuzzy approach to model the engine. Based on the virtual engine model, the capability of generating power at arbitrary engine operating point can be obtained fast and accurately, which is applicable to implement in real time.

Funding

This work was co-funded by Innovate UK, under a grant for the Low Carbon Vehicle IDP4 Programme (TP14/LCV/6/I/BG011L). Innovate UK is an executive body established by the United Kingdom Government to drive innovation. It promotes and invests in research, development and the exploitation of science, technology and new ideas for the benefit of business - increasing sustainable economic growth in the UK and improving quality of life. This work was also co-funded by Engineering and Physical Sciences Research Council of UK under the EPSRC Innovation Fellowship scheme (EP/S001956/1).

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

SAE World Congress

Citation

ZHAO, D., GU, W. and MASON, B., 2019. Real time energy management of electrically turbocharged engines based on model learning. SAE Technical Papers, 2019-01-1056.

Publisher

© SAE International

Version

  • AM (Accepted Manuscript)

Publisher statement

This paper was accepted for publication in the journal SAE Technical Papers and the definitive published version is available at https://doi.org/10.4271/2019-01-1056

Acceptance date

2019-01-15

Publication date

2019-04-02

Notes

This paper was presented at WCX SAE World Congress Experience, Detroit, USA, 9-11 April 2019.

ISSN

0148-7191

Language

  • en

Location

Detroit, USA

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