Research on the Statistical Method of Real Driving Emission Data of Commercial Vehicle under Low Load

Current emission regulation in China (National VI b) adopts the work-based window (WBW) method to statistically analyze PEMS experimental data. This method cannot fully account for experimental data under low load and cold start conditions. In light of this, this paper proposes a statistical method for low-load condition experimental data. Firstly, the adaptability of the WBW method to low-load condition experimental data is analyzed. Secondly, the representativeness and authenticity of statistical results from different methods are compared. The results indicate that when the power threshold of the WBW method is set at 20%, the effective window qualification rate in six experiments is less than 40%. And as the load decreases, the power threshold required to meet regulatory requirements needs to be further reduced, meaning more low-power data points are discarded. The WBW method eliminates many low output power data points with high CO and NO_x emissions from test data on an urban road section with low driving speed, significantly underestimating the CO and NO_x emission data under low load conditions, with NO_x emissions 56.8% lower than the cumulative averaging (CA) method results. It is recommended to use the CA method for calculating CO and NO_x emissions under low load conditions.