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Real-time energy management of the electric turbocharger based on explicit model predictive control

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journal contribution
posted on 26.03.2019, 14:27 by Dezong Zhao, Richard Stobart, Byron Mason
The electric turbocharger is a promising solution for engine downsizing. It provides great potential for vehicle fuel efficiency improvement. The electric turbocharger makes engines run as hybrid systems so critical challenges are raised in energy management and control. This paper proposes a real-time energy management strategy based on updating and tracking of the optimal exhaust pressure setpoint. Starting from the engine characterisation, the impacts of the electric turbocharger on engine response and exhaust emissions are analysed. A multivariable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The two-level controller works in a highly efficient way to fulfill the optimal energy management. This strategy has been validated in physical simulations and experimental testing. Excellent tracking performance and sustainable energy management demonstrate the effectiveness of the proposed method.

Funding

This work was supported in part by the Low Carbon Vehicle IDP4 Programme of Innovate UK under Grant TP14/LCV/6/I/BG011L and in part by the EPSRC-UKRI Innovation Fellowship scheme of Engineering and Physical Sciences Research Council of U.K. under Grant EP/S001956/1.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Electronics

Volume

67

Issue

4

Pages

3126 - 3137

Citation

ZHAO, D., STOBART, R. and MASON, B., 2019. Real-time energy management of the electric turbocharger based on explicit model predictive control. IEEE Transactions on Industrial Electronics, 67 (4), pp.3126-3137.

Publisher

© IEEE (Institute of Electrical and Electronics Engineers)

Version

AM (Accepted Manuscript)

Acceptance date

08/03/2019

Publication date

2019-04-15

Notes

© 2018 IEEE. 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.

ISSN

0278-0046

Language

en