林建新, 云旭, 李京冕, 商鹏飞. 基于二次卡尔曼滤波修正的尾气排放分布[J]. 北京工业大学学报, 2020, 46(3): 311-319. DOI: 10.11936/bjutxb2018070036
    引用本文: 林建新, 云旭, 李京冕, 商鹏飞. 基于二次卡尔曼滤波修正的尾气排放分布[J]. 北京工业大学学报, 2020, 46(3): 311-319. DOI: 10.11936/bjutxb2018070036
    LIN Jianxin, YUN Xu, LI Jingmian, SHANG Pengfei. Automobile Emission Distribution Based on the Quadratic Kalman Filter Correction[J]. Journal of Beijing University of Technology, 2020, 46(3): 311-319. DOI: 10.11936/bjutxb2018070036
    Citation: LIN Jianxin, YUN Xu, LI Jingmian, SHANG Pengfei. Automobile Emission Distribution Based on the Quadratic Kalman Filter Correction[J]. Journal of Beijing University of Technology, 2020, 46(3): 311-319. DOI: 10.11936/bjutxb2018070036

    基于二次卡尔曼滤波修正的尾气排放分布

    Automobile Emission Distribution Based on the Quadratic Kalman Filter Correction

    • 摘要: 监测机动车尾气排放并制定科学减排措施迫在眉睫,而如何反映由交通需求变化引起排放分布变化是建立尾气排放分布模型的核心问题.按照"交通需求推演-机动车比功率参数确定-交通排放"的思路,利用机动车比功率参数标定交通需求与尾气排放之间的量化关系.以交通需求数据为基础,通过二次卡尔曼滤波修正交通流状态数据和交通分布数据,并通过动态交通分配获取实时车辆工况参数,与典型车型工况曲线匹配,确定车辆比功率在不同速度区间分布,而后,将机动车行驶特征工况参数代入国际车辆排放(international vehicle emission,IVE)模型中确定排放因子,计算得到区域内机动车尾气排放强度.研究表明,以15 min为预测周期,利用二次卡尔曼滤波估计交通需求的平均相对误差为8.89%,且利用IVE尾气模型模拟具有较好的可靠性.预测结果显示,实现基于动态交通需求的行驶工况所构建的交通污染分布模型具有可行性,且二次卡尔曼滤波修正提供了精度的保证,该推演数据可用于分析尾气分布,评价交通改善措施对尾气排放影响,为制定减排策略提供依据.

       

      Abstract: It is extremely urgent to monitor vehicle exhaust emissions and formulate scientific emission reduction measures. How to respond to changes in emissions distribution caused by changes in traffic demands is the core issue in establishing a distribution model for exhaust emissions. According to the idea of "transfer of traffic demand-determination of vehicle specific power parameters-traffic emissions", the vehicle's specific power parameters were used to calibrate quantitative relationship between traffic demands and the exhaust gas emission. Based on the traffic demand data, the traffic flow state data and traffic distribution data were corrected by the quadratic Kalman filter (QKF), and the real-time vehicle operating condition parameters were obtained through dynamic traffic assignment, which matches the typical vehicle operating condition curve to determine the vehicle specific power at different speeds. The interval distribution, and then the vehicle driving characteristic parameters were substituted into the international vehicle emission (IVE) model to determine the emission factor, and the vehicle exhaust emissions in the area were calculated. Results show that the average relative error of traffic demand is estimated by 8.89% using the QKF for 15 minutes, and the simulation with IVE tail gas model has better reliability. The prediction results show that it is feasible to realize the traffic pollution distribution model constructed based on the driving conditions of dynamic traffic demand, and the QKF correction provides the guarantee of accuracy. The deductive data can be used to analyze the distribution of exhaust gas and evaluate traffic improvement measures. The impact on tail gas emissions provides a basis for developing emission reduction strategies.

       

    /

    返回文章
    返回