机动车尾气排放因子及排放测试方法的研究

发布时间：2018-05-27 00:35

本文选题：机动车 + 尾气排放　；参考：《浙江工业大学》2016年硕士论文

【摘要】：机动车尾气排放已成为城市空气污染的主要来源之一。影响城市道路机动车尾气排放总量及污染程度的关键因素为机动车尾气排放因子和实际交通行驶状况等。因此本文以排放因子为切入点,结合实际交通行驶状态下的机动车尾气测量,从微观和宏观的角度深入研究了机动车实际行驶工况下的尾气排放因子。为获得实际交通行驶工况下的排放数据,从车载测量、隧道测试以及路边测量对尾气排放进行了微观和宏观角度的排放测试。研究成果能够为准确分析城市机动车污染排放状况、建立尾气排放控制策略,以及发展城市低碳交通提供理论依据。首先,在分析国内外现有模型的基础上,以及分析相关修正研究的结果,得出现有模型的不足之处以及用修正方法来研究机动车尾气污染物排放存在的缺点,之后对机动车各种污染物的内在成因进行了分析,最后得出研究机动车污染物排放的方法是结合实测数据入手对污染物从微观和宏观的角度进行预测。之后以杭州市为例,对污染物的排放进行了从微观和宏观角度的测试,测试结果表明:微观的排放数据从一定程度上反映了实际交通行驶状况,宏观的排放数据受气象条件的影响,有必要选择合适的时间段来研究机动车尾气排放。然后,以排放测试的结果为基础,根据油耗与各污染物的数据关系得出油耗和污染物之间的回归模型,再通过对预测模型的原理性推导得出影响因素与燃油消耗之间的量化关系,最终得到微观综合预测模型。最后用实测数据对预测模型进行了实验验证,结果表明:微观预测模型首次对污染物CO和NO的预测模拟值和实测值之间的平均误差分别为14.3%和12.8%,实现了较好的预测效果,为进一步从微观角度研究燃油消耗和排放之间的关系奠定了很好的基础。最后,从宏观的角度对机动车尾气排放因子进行了研究,根据隧道法的原理,对实验的环境进行了推广,选取了早高峰时段,对污染物隧道测试的宏观计算方法进行了改进推广,最后对结果进行多元回归从而得到相应车型的污染物排放。结果表明:宏观模型实现了很好的模拟效果,隧道测试方法得到了改进推广。
[Abstract]:Vehicle exhaust emission has become one of the main sources of urban air pollution. The key factors affecting the total amount and pollution degree of motor vehicle emissions on urban roads are vehicle exhaust emission factors and actual traffic driving conditions. So this paper takes emission factor as the breakthrough point, combines the vehicle exhaust measurement under the actual traffic driving state, from the microscopic and macroscopic angle, deeply studies the exhaust emission factor under the actual driving condition of the vehicle. In order to obtain the emission data under the actual traffic driving conditions, the emission measurements from the microscopic and macroscopic angles were carried out from vehicle measurement, tunnel test and roadside measurement. The research results can provide a theoretical basis for the accurate analysis of urban motor vehicle pollution, the establishment of emission control strategies and the development of urban low-carbon traffic. First of all, on the basis of analyzing the existing models at home and abroad, as well as the analysis of the results of the relevant correction research, the shortcomings of the existing models and the shortcomings of using the modified method to study the emission of vehicle exhaust pollutants are obtained. After analyzing the internal causes of vehicle pollutants, it is concluded that the method of studying vehicle pollutant emission is to predict the pollutant from the micro and macro angle combining with the measured data. Then, taking Hangzhou as an example, the emission of pollutants is tested from the micro and macro angles. The results show that the microscopic emission data reflect the actual traffic conditions to some extent. The macro emission data are affected by meteorological conditions, so it is necessary to select a suitable time period to study vehicle exhaust emissions. Then, based on the results of the emission test, the regression model between oil consumption and pollutants is obtained according to the relationship between oil consumption and each pollutant. Then the quantitative relationship between the influencing factors and fuel consumption is obtained by deducing the principle of the prediction model, and finally the micro comprehensive prediction model is obtained. Finally, the experimental results show that the average error between the simulated and measured values of CO and no is 14.3% and 12.8% for the first time. It lays a good foundation for the further study of the relationship between fuel consumption and emissions from a micro perspective. Finally, the vehicle exhaust emission factor is studied from a macro perspective. According to the principle of tunnel method, the experimental environment is extended, and the early peak period is selected. The macroscopic calculation method of pollutant tunnel test is improved and extended. Finally, the multi-element regression is carried out to get the pollutant emission of the corresponding vehicle model. The results show that the macro model achieves a good simulation effect and the tunnel testing method is improved and extended.
【学位授予单位】：浙江工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U467.48

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