Determination of exhaust emission characteristics in the RDE test using the Monte Carlo method

Abstract

The article presents a method of determining the characteristics of exhaust emissions and fuel mass consumption in real driving conditions based on a single test using the Monte Carlo method. The exhaust emission characteristics used are the relations between the emissions and the average vehicle speed, and the characteristic of the fuel mass consumption is the dependence of the fuel mass consumption at the average vehicle speed. The results of empirical research of a passenger car with a spark-ignition engine in the RDE test were used. The use of the Monte Carlo method made it possible to select the initial and final moments of averaging the process values, thanks to which it was possible to determine the discrete values of the characteristics for various values of average vehicle speeds. The determined discrete characteristics of the particulate mass and number emissions and fuel mass consumption relative to the average vehicle speed were approximated by polynomial functions of the second and third degree. The determined discrete characteristics, presented as sets of points, were characterized by a relatively small dis-persion in relation to their polynomial approximations. The average relative deviation of the points of discrete characteristics from the value of the polynomial was in most cases small less than 4%, only in the case of the number of particles emitted deviated from this, as the average relative deviation of the measured points from the determined polynomial was nearly 14%. Combined with the results of RDE empirical studies, the Monte Carlo method proved to be an effective method for determining the characteristics of exhaust emissions, measured in real vehicle operating conditions. The main advantage of the proposed method was a significant reduction in the actual workload necessary to carry out the empirical research where it became possible to determine the charac-teristics in a large range of vehicle average speed values with just one drive test. Using standard methods of meas-uring this type of data, it would be necessary to conduct multiple tests, driving at different average vehicle speeds.