# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 7 Conclusions and Discussion  
Euro 6 is a significant improvement over the previous standard owing to the development of after-treatment technologies. Hence, \(\rm NO_{x}\) concentrations from Euro 6 diesel cars may be significantly different from previous standards during real-world driving. Accordingly, a comprehensive approach in this study was necessary to analyze the \(\rm NO_{x}\) concentrations from a Euro 6 diesel engine, considering three types of factors (road environmental, atmospheric, and after-treatment performance factors). To this end, three tested vehicles (Euro 6 light-duty diesel vehicles), equipped with PEMSs, were used to collect data during real-world driving. Then, an ANN model was established, and the results imply that the accuracy obtained was appropriate for modeling. In terms of model application, relative variable importance and analyses for \(\rm NO_{x}\) concentrations were performed using the ANN model. The relative variable importance results indicated that after-treatment performance factors were highly associated with \(\rm NO_{x}\) concentrations, whereby the weight of the EGR rate was the main factor, with the most significant effect on the \(\rm NO_{x}\) concentration value. Following this, the ambient temperature, exhaust temperature, and relative temperature had considerable effects on \(\rm NO_{x}\) concentrations, while the VSP had the smallest effect on the \(\rm NO_{x}\) concentrations, contributing  
Figure 8: \(\rm NO_{x}\) concentrations predicted as a function of exhaust temperature and EGR rate according to speed classification in the ANN. about 0.8 of the total effect. In fact, these road factors were the main factors influencing \(\rm NO_{x}\) concentrations in the previous standards. In the analysis of the \(\rm NO_{x}\) concentrations for each variable, the vehicle speed, acceleration, and \(\rm NO_{x}\) concentrations were analyzed as a function of speed classifications (below 60 km/h, 60-90 km/h, and above 90 km/h). The acceleration rates at below 60 km/h were widely distributed; however, this acceleration portion showed a low influence on \(\rm NO_{x}\) concentrations. Meanwhile, higher \(\rm NO_{x}\) concentrations were predicted at high speed. In the \(\rm NO_{x}\) concentration obtained as a function of ambient temperature and relative humidity, higher \(\rm NO_{x}\) concentrations were estimated at low ambient temperatures and high relative humidity within a specific range. In particular, the higher \(\rm NO_{x}\) concentrations were estimated at ambient temperatures around 20-27 \({}^{\circ}\)C and relative humidity of 50-70%. Furthermore, relatively higher \(\rm NO_{x}\) concentrations were estimated at low speeds, and \(\rm NO_{x}\) concentrations were higher during the cold test. Lastly, in the analysis of the \(\rm NO_{x}\) concentration with respect to the after-treatment performance factors, the predicted \(\rm NO_{x}\) concentration increased with increasing exhaust temperature up to 200 \({}^{\circ}\)C. Furthermore, the predicted \(\rm NO_{x}\) concentration tended to decrease as the EGR rate increased in the \(\rm NO_{x}\) concentration obtained as a function of the EGR rate. In summary, the highlights of this study are shown below. * Analysis of \(\rm NO_{x}\) emissions from Euro 6 diesel engines was conducted. * Tests and analyses were performed under real-world driving conditions. * Road environmental, atmospheric, and after-treatment factors were collected. * The proposed ANN method predicts pollutant emissions (\(\rm NO_{x}\)) with good performance. * The relative importance of each predictor on \(\rm NO_{x}\) emission values was derived. This research, indeed, had data limitations. For model estimation, various data types are needed that include the different road conditions. The RDE test in this study was conducted within the boundaries specified by the RDE requirements; certain driving conditions were excluded by setting the boundary segments in relation to dynamic driving.