Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle

• Main Authors: Jonghak Lee, Sangil Kwon, Hyung Jun Kim, Jihoon Keel, Taekwan Yoon, Jongtae Lee
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Applied Sciences
• Subjects: artificial neural network; real driving emission; portable emissions measurement system; nitrogen oxide; light-duty diesel vehicle; machine learning
• Online Access: https://www.mdpi.com/2076-3417/11/9/3758

Euro 6 standards impose stringent nitrogen oxide (NO_x) emission limits on diesel cars. NO_x emissions are significantly different between Euro 6 diesel cars and the previous standards in real-world driving. In this research, the NO_x concentrations of Euro 6 diesel engines during real-world driving were studied considering various factors. Real driving emission (RDE) tests were conducted using vehicles equipped with portable emissions measurement systems. Urban, rural, and motorway test routes were utilized. Road environment, atmospheric, and after-treatment performance factors were collected in each case. An artificial neural network was used for evaluation using RDE test data and various statistical parameters. It was found that the proposed method predicted the pollutant emissions effectively. Lastly, the relative importance of each predictor was derived, and the NO_x concentrations were analyzed. These approaches provide accurate emission information for an environmental effect evaluation that reflects more realistic road conditions.