0
research-article

Computational Intelligence Non-model-based Calibration Approach for Internal Combustion Engines

[+] Author and Article Information
HE MA

Research Fellow, School of Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
mahe0502@gmail.com

Ziyang Li

School of Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
z.li.6@pgr.bham.ac.uk

Mohammad Tayarani

Research Fellow, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
mo_tayarani@yahoo.com

Guoxiang Lu

Research Fellow, School of Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
g.lu.2@bham.ac.uk

Hongming Xu

Professor, CEng, FIMechE, FHEA, FSAE, School of Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
h.m.xu@bham.ac.uk

X. Yao

Professor, FIEEE, Natural Computation Group, School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, U.K.
x.yao@cs.bham.ac.uk

1Corresponding author.

ASME doi:10.1115/1.4037835 History: Received April 20, 2016; Revised August 17, 2017

Abstract

Over the past 20 years, with the increase in the complexity of engines, and the combinatorial explosion of engine variables space, the engine calibration process has become more complex, costly and time consuming. As a result, an efficient and economic approach is desired. For this purpose, many engine calibration methods are under development in OEMs and universities. The state-of-the-art model-based steady state Design of Experiments (DoE) technique is mature and is used widely. However, it is very difficult to further reduce the measurement time. Additionally, the increasingly high requirements of engine model accuracy, and robust testing process with high data quality by high quality testing facility, also constrain the further development of model-based DoE engine calibration. This paper introduces a new computational intelligence approach to calibrate internal combustion engine without the need for an engine model. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 GDI engine test bench, a Simulink real-time based embedded system was developed and implemented to engine ECU through Rapid Control Prototyping (RCP) and external ECU bypass technology. Experimental validations prove that the developed engine calibration approach is capable of automatically finding the optimal engine variable set which can provide the best fuel consumption and PM emissions, with good accuracy and high efficiency. The introduced engine calibration approach does not rely on either the engine model, or massive test bench experimental data. It has great potential to improve the engine calibration process for industries.

Copyright (c) 2017 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In