Machine Learning Techniques for Classification of Combustion Events under Homogeneous Charge Compression Ignition (HCCI) Conditions

This research evaluates the capability of data-science models to classify the combustion events in Cooperative Fuel Research Engine (CFR) operated under Homogeneous Charge Compression Ignition (HCCI) conditions. A total of 10,395 experimental data from the CFR engine at the University of Michigan (UM), operated under different input conditions for 15 different fuel blends, were utilized for the study. The combustion events happening under HCCI conditions in the CFR engine are classified into four different modes depending on the combustion phasing and cyclic variability (COV_imep). The classes are; no ignition/high COV_imep, operable combustion, high MPRR, and early CA50. Two machine learning (ML) models, K-nearest neighbors (KNN) and Support Vector Machines (SVM), are compared for their classification capabilities of combustion events. Seven conditions are used as the input features for the ML models viz. Research Octane Number (RON) of fuel, Sensitivity of fuel (S), fuel rate (J/L/cycle), oxygen mole fraction, intake temperature and pressure, and compression ratio. The evaluation metric used in this study is micro-precision, which is ideal for a multi-classification problems given the limited amount of data available and the nonuniformity of the data set. The proposed SVM method achieved a higher average classification accuracy of 93.5% and better generalization performance than KNN (mean accuracy of 89.2%). A feature analysis was performed to deduce the most sensitive input features, and the model also facilitates building of correlations for the dependence of HCCI engine stability on various input features. This ML approach employed here can be extended to help engine researchers implement a design of experiments (DOE) beforehand, carefully targeting the right input conditions for fuel to enable controlled combustion phasing in the HCCI regime.