# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
###### Abstract  
Euro 6 standards impose stringent nitrogen oxide (NO\({}_{\text{x}}\)) emission limits on diesel cars. NO\({}_{\text{x}}\) emissions are significantly different between Euro 6 diesel cars and the previous standards in real-world driving. In this research, the NO\({}_{\text{x}}\) 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\({}_{\text{x}}\) concentrations were analyzed. These approaches provide accurate emission information for an environmental effect evaluation that reflects more realistic road conditions. artificial neural network; real driving emission; portable emissions measurement system; nitrogen oxide; light-duty diesel vehicle; machine learning +
Footnote †: journal: Medical  
conditions [16]. Owing to these advantages, analyses of gaseous emission characteristics from diesel vehicles under realistic road conditions have been conducted using the RDE test procedure [17; 18]. There has been a considerable amount of research on the factors affecting \(\mathrm{NO_{x}}\) emissions of diesel cars under realistic road conditions with the previous standards (prior to Euro 6). These studies indicated that the \(\mathrm{NO_{x}}\) emissions from diesel cars in previous standards were significantly affected by the road grade, showing a decline with decreasing vehicle speed during uphill driving (ranging from 3.8 to 5.3 g\(\cdot\)L\({}^{-1}\) on the basis of vertical grades) [19]. Furthermore, the driving pattern factors, such as speed and acceleration, were observed to affect the environment because of \(\mathrm{NO_{x}}\) emissions. These factors related to road environmental parameters could have significant effects on \(\mathrm{NO_{x}}\) emissions [20; 21]. For instance, vehicular traffic leads to an increase in air pollutants, which is one of the main sources in internal combustion engine processes. Meanwhile, Euro 6 diesel vehicles are subjected to emission control technologies, and the \(\mathrm{NO_{x}}\) emissions from Euro-6 diesel vehicles can be well controlled by utilizing \(\mathrm{NO_{x}}\) control technologies such as EGR and combustion and fuel injection systems for clean combustion engines [8]. Recently, academia and industry have researched and invested in conventional diesel combustion to develop an innovative combustion system for improving the \(\mathrm{CO_{2}}\) and the \(\mathrm{NO_{x}}\) soot tradeoffs; examples include advanced combustion concepts, specific bowl design, and advanced fuel injection systems, which are becoming a part of the ICE context [22; 23]. All these strategies can effectively clean the exhaust gases, as well as contribute to an improvement in efficiency and performance for \(\mathrm{NO_{x}}\) reductions in real road conditions [24; 25]. This finding implies that road environmental factors have little impact on \(\mathrm{NO_{x}}\) in the Euro 6 standard. However, the extent to which these factors affect \(\mathrm{NO_{x}}\) emissions in real-world driving is unknown. Similar studies for Euro 6 diesel vehicles are lacking because of the more complex interactive mechanisms of \(\mathrm{NO_{x}}\) formation in Euro 6 diesel vehicles than in the previous standards. Thus, the current method cannot be applied to road networks in South Korea for \(\mathrm{NO_{x}}\) emissions from Euro 6 diesel vehicles.