摘要：Several studies in literature have shown how real-world emissions strongly depend on driving condition, driving style, ambient temperature and humidity, etc. so that they are significantly different from the values measured on test benches over standard driving cycles. This concern, together with the so-called Diesel-gate, has caused the introduction in Europe of an innovative procedure for the registration of vehicle based on real driving emissions (RDE) measured with a portable emission measurement system (PEMS). PEMS devices are bulky and very expensive, therefore they cannot be extensively for an actual real time monitoring of emissions. To solve this problem, the present work proposes a Neural Network model based on the interpolation of the time-histories of driving conditions (speed, altitude, ambient temperature, humidity and pressure) and emissions measured on a diesel start-and-stop vehicle while performing a series of RDE tests. Two different approaches are proposed. The first one calculates the emissions on the basis of the vehicle motion (speed and altitude profile, ambient conditions). The second one models the engine block using as input the ambient conditions, the load and the rpm of the engine as derived from the OBD-II scanner. The output of both models are the flow rates and cumulated values of CO 2 and NO x . Note that the inputs of the two models are signal that can easily obtained on-board without additional sensors.
