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Grey-Box Modeling for HCCI Engine Control

[+] Author Affiliations
M. Bidarvatan, M. Shahbakhti

Michigan Technological University, Houghton, MI

Paper No. ICEF2013-19097, pp. V001T05A006; 10 pages
doi:10.1115/ICEF2013-19097
From:
  • ASME 2013 Internal Combustion Engine Division Fall Technical Conference
  • Volume 1: Large Bore Engines; Advanced Combustion; Emissions Control Systems; Instrumentation, Controls, and Hybrids
  • Dearborn, Michigan, USA, October 13–16, 2013
  • Conference Sponsors: Internal Combustion Engine Division
  • ISBN: 978-0-7918-5609-3
  • Copyright © 2013 by ASME

abstract

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of Homogeneous Charge Compression Ignition (HCCI) engines. Grey-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural networks models to form a serial architecture grey-box model. The resulting model can predict three major HCCI engine control outputs including combustion phasing, Indicated Mean Effective Pressure (IMEP), and exhaust gas temperature (Texh). The grey-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate the grey-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the grey-box model predicts combustion phasing, IMEP, and Texh with an average error less than 1 crank angle degree, 0.2 bar, and 6 °C respectively. The grey-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

Copyright © 2013 by ASME

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