# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 6 Model Application
### Analysis of NOx Concentrations  
In this section, a study on how the \(\text{NO}_{\text{x}}\) concentration would be estimated during real-world driving using data derived from the ANN was conducted. The analysis of the \(\text{NO}_{\text{x}}\) concentrations was performed using data derived from ANN. Figure 6 presents the results of vehicle speed, acceleration, and \(\text{NO}_{\text{x}}\) concentrations as a function of speed classification in the ANN (below 60 km/h (urban), 60-90 km/h (rural), and above 90 km/h (motorway)). The deviation of acceleration/deceleration was relatively large on urban roads because of traffic congestion in urban areas. Meanwhile, the deviation on rural roads and motorways was relatively lower. Figure 5: Relative variable importance results in ANN. The speed distribution below 60 km/h represented a large portion. In terms of the acceleration patterns for speed classification, the acceleration rates were more widely estimated below 60 km/h than for other speed classifications. However, the NO\({}_{\text{x}}\) concentration was less influenced by the acceleration rate in this case. Furthermore, it was found that higher NO\({}_{\text{x}}\) concentrations were predicted at speeds higher than 90 km/h. This result is assumed to be due to the occurrence of high temperature as a function of high-speed heating of the vehicle engine on the motorway. So far, dependences on atmospheric factors such as ambient temperature and relative humidity have been overlooked in the inventory of the latest Euro 6 emission standard, but these dependences are important from an air quality perspective [60]. Accordingly, the NO\({}_{\text{x}}\) concentration in terms of ambient temperature and relative humidity, which were regarded as main factors for NO\({}_{\text{x}}\) in the analysis of relative variable importance, were analyzed in this study. Figure 7 presents the results of predicted NO\({}_{\text{x}}\) concentrations as a function of ambient temperature and relative humidity in ANN, where the red circled region presents the section with a relatively high concentration according to speeds and engine condition (cold or hot). It was found that higher NO\({}_{\text{x}}\) concentrations tended to be estimated at low ambient temperatures and high relative humidity within the red dotted circle in Figure 7. In particular, higher NO\({}_{\text{x}}\) concentrations were estimated at ambient temperatures around 20-27 \({}^{\circ}\)C and relative humidity around 50-70%. Figure 6: Vehicle speed, acceleration, and NO\({}_{\text{x}}\) concentrations as a function of speed classification in the ANN. In the analysis by speed classification, relatively higher NOx concentrations were estimated at low speeds (below 60 km/h; urban area) because of signaled intersections and traffic jams in urban area, which lead to frequent acceleration and braking. Higher NOx concentrations were estimated during the cold phase in the analysis by test type, as the unbalanced circumstances between heat transfer and self-ignition reactions lead to higher NOx emissions in the cold starting process. During this time, combustion suffers from a lack of stability reflected in misfires due to deterioration of the Urea-SCR and after-treatment systems; then, catalytic converters generate smoke pollution, which may escape from the uncontrolled system as tailpipe emissions, when engine block and coolant temperature are low. Lastly, according to the relative variable importance analysis, after-treatment performance factors were proven to be major factors among all the variables, in terms of affecting NOx concentration. Therefore, the NOx concentrations as a function of the exhaust temperature and EGR rate were evaluated in this study. Figure 8 presents the results of NOx concentrations predicted as a function of exhaust temperature and EGR rate according to speed classification in the ANN.