A System Identification Framework for Modeling Complex Combustion Dynamics Using Support Vector Machines

  • Vijay Manikandan Janakiraman
  • XuanLong Nguyen
  • Jeff Sterniak
  • Dennis Assanis
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 283)


Machine Learning is being widely applied to problems that are difficult to model using fundamental building blocks. However, the application of machine learning in powertrain modeling is not common because existing powertrain systems have been simple enough to model using simple physics. Also, black box models are yet to demonstrate sufficient robustness and stability features for widespread powertrain applications. However, with emergence of advanced technologies and complex systems in the automotive industry, obtaining a good physical model in a short time becomes a challenge and it becomes important to study alternatives. In this chapter, support vector machines (SVM) are used to obtain identification models for a gasoline homogeneous charge compression ignition (HCCI) engine. A machine learning framework is discussed that addresses several challenges for identification of the considered system that is nonlinear and whose region of stable operation is very narrow.


Support vector machine Identification Combustion Homogeneous charge compression ignition HCCI Neural networks Nonlinear regression Engine model Control model 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vijay Manikandan Janakiraman
    • 1
  • XuanLong Nguyen
    • 1
  • Jeff Sterniak
    • 2
  • Dennis Assanis
    • 3
  1. 1.University of MichiganAnn ArborUSA
  2. 2.Robert Bosch LLCFarmingtonUSA
  3. 3.Stony Brook UniversityNYUSA

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