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Authors: Vijay Manikandan Janakiraman 1 ; Jeff Sterniak 2 and Dennis Assanis 3

Affiliations: 1 University of Michigan, United States ; 2 Robert Bosch LLC, United States ; 3 Stony Brook University, United States

Keyword(s): Support Vector, Identification, Combustion, Homogeneous Charge Compression Ignition, HCCI, Neural Networks, Nonlinear Regression, Engine Model, Control Model.

Related Ontology Subjects/Areas/Topics: Engineering Applications ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; System Identification ; System Modeling

Abstract: Homogeneous charge compression ignition (HCCI) is a promising technology for Internal Combustion Engines to improve efficiency and reduce nitrogen oxides emissions. Control of HCCI combustion is often model-based, and it is vital to have a good model of the engine to make control decisions. The HCCI engine is characterized by complex chemical kinetics whose physical modeling is difficult and laborious. Identification is an effective alternative to quickly develop control oriented models for such systems. This paper formulates a Support Vector Regression (SVR) methodology for developing identification models capturing HCCI combustion behavior. Measurable quantities from the engine such as net mean effective pressure (NMEP) and crank angle at 50% mass fraction burned (CA50) can be used to characterize and control the HCCI engine and are considered for identification in this study. The selected input variables include injected fuel mass (FM) and valve events {intake valve opening (IVO), exhaust valve closing (EVC)}. Transient data from a gasoline HCCI engine recorded at stable HCCI conditions is used for training, validating and testing the SVR models. Comparisons with the experimental results show that SVR with Gaussian kernels can be a powerful approach for identification of a complex combustion system like the HCCI engine. (More)

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Paper citation in several formats:
Janakiraman, V.; Sterniak, J. and Assanis, D. (2012). Support Vector Machines for Identification of HCCI Combustion Dynamics. In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-8565-21-1; ISSN 2184-2809, SciTePress, pages 385-393. DOI: 10.5220/0004035903850393

@conference{icinco12,
author={Vijay Manikandan Janakiraman. and Jeff Sterniak. and Dennis Assanis.},
title={Support Vector Machines for Identification of HCCI Combustion Dynamics},
booktitle={Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2012},
pages={385-393},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004035903850393},
isbn={978-989-8565-21-1},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Support Vector Machines for Identification of HCCI Combustion Dynamics
SN - 978-989-8565-21-1
IS - 2184-2809
AU - Janakiraman, V.
AU - Sterniak, J.
AU - Assanis, D.
PY - 2012
SP - 385
EP - 393
DO - 10.5220/0004035903850393
PB - SciTePress