Using OEM-2100 to build light-duty vehicle testing platform and test on actual road, vehicle emission factors were analyzed by regression and neural network method, the emission model between speed, acceleration and mass emission rate was established, and the emissions data error based on actual road emissions was calculated. The results indicate that the establishment of emission model can predict the actual instantaneous mass emission rate, the total error of speed regression model, and the binary regression model and the neural network model can predict vehicle emissions with the error less than 17%, while the second based error is about 30%, and the emission model can merge with traffic simulation model to evaluate traffic control measures under different operating conditions in motor vehicle emissions.%研究采用车载质量排放测试仪器OEM-2100为基础搭建轻型车车载排放测试试验平台,并在实际道路上进行大量试验；基于试验结果分析实际道路轻型车排放影响因素,通过回归方法和神经网络方法建立速度、加速度与轻型车质量排放率之间的模型；基于实际道路排放数据进行模型误差计算.研究结果表明:所建立的排放模型可以预测实际道路上的瞬时排放率变化情况,速度回归模型、二元回归模型和神经网络模型对轻型车排放进行预测分析的整体误差小于17％,而每秒的误差基本上也在30％以内,并可与交通仿真模型相互融合,从而评价不同交通管控措施下轻型车运行状况变化带来的排放差异.
展开▼
