Support vector machine based emissions modeling using particle swarm optimization for homogeneous charge compression ignition engine. (February 2023)

• Record Type: Journal Article
• Authors: Gordon, David; Norouzi, Armin; Blomeyer, Gero; Bedei, Julian; Aliramezani, Masoud; Andert, Jakob; Koch, Charles R
• Is Part Of: International journal of engine research. Volume 24:Number 2(2023)
• Page Start: 536
• Page End: 551
• Publication Date: 2023-02
• Subjects: Emission predictive modeling -- homogeneous charge compression ignition -- support vector machine -- machine learning -- particle swarm optimization -- artificial neural network; Engines -- Periodicals; 629.25
• DOI: 10.1177/14680874211055546
• Languages: English
• ISSNs: 1468-0874

Abstract

The internal combustion engine faces increasing societal and governmental pressure to improve both efficiency and engine out emissions. Currently, research has moved from traditional combustion methods to new highly efficient combustion strategies such as Homogeneous Charge Compression Ignition (HCCI). However, predicting the exact value of engine out emissions using conventional physics-based or data-driven models is still a challenge for engine researchers due to the complexity the of combustion and emission formation. Research has focused on using Artificial Neural Networks (ANN) for this problem but ANN's require large training datasets for acceptable accuracy. This work addresses this problem by presenting the development of a simple model for predicting the steady-state emissions of a single cylinder HCCI engine which is created using an metaheuristic optimization based Support Vector Machine (SVM). The selection of input variables to the SVM model is explored using five different feature sets, considering up to seven engine inputs. The best results are achieved with a model combining linear and squared inputs as well as cross correlations and their squares totaling 26 features. In this case the model fit represented by R 2 values were between 0.72 and 0.95. The best model fits were achieved for CO and CO2, while HC and NOx models have reduced model performance. Linear and non-linear SVM models were then compared to an ANN model. This comparison showed that SVM based models were more robust to changes in feature selection and better able to avoid local minimums compared to the ANN models leading to a more consistent model prediction when limited training data is available. The proposed machine learning based HCCI emission models and the feature selection approach provide insight into optimizing the model accuracy while minimizing the computational costs.