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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >NO Sensor Reading Correction in Diesel Engine Selective Catalytic Reduction System Applications
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NO Sensor Reading Correction in Diesel Engine Selective Catalytic Reduction System Applications

机译：柴油机选择性催化还原系统中NO传感器读数校正的应用

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摘要

In this paper, we investigate NO sensor reading correction in diesel engine selective catalytic reduction (SCR) system applications. It is well known that the urea-based SCR system is promising to remove engine-out NO emissions and achieve strict legislative emission regulations. However, if the urea is overdosed, the system also leads to ammonia slip in the tailpipe. In order to control the NO emissions and constrain the ammonia slip, a feedback loop with NO and ammonia sensors may be needed. An interesting phenomenon in the closed loop is that a well calibrated NO sensor does not work well in the urea-based SCR systems. For this typical application, there may be several sources for the NO sensor reading mismatching issues. Due to the existence of the gaseous ammonia, the NO sensor reading is approximately a combination of the actual NO concentration and the ammonia concentration. Another phenomenon is that the influence of the ammonia concentration to the NO sensor reading is significantly different with respect to different temperatures. Based on such observations and the importance of the NO concentration reading, we devote to develop an algorithm to correct the NO sensor reading. With considerable experimental data which cover most of the typical SCR operating temperature range, we obtain the relationship between the cross-sensit- vity factor and the temperature by employing the adaptive-network-based fuzzy inference system (ANFIS). The developed fuzzy inference system is then applied to experimental validation tests. Compared with the existing extended Kalman filter (EKF), the developed fuzzy inference system has prominent advantages on the reading correction performance.
机译：在本文中，我们研究了NO传感器读数校正在柴油机选择性催化还原（SCR）系统中的应用。众所周知，基于尿素的SCR系统有望消除发动机排出的NO排放并达到严格的立法排放法规。但是，如果尿素过量，系统也会导致排气管中的氨泄漏。为了控制NO排放并限制氨泄漏，可能需要带有NO和氨传感器的反馈回路。闭环中一个有趣的现象是，校准良好的NO传感器在基于尿素的SCR系统中无法正常工作。对于此典型应用，可能有多个原因导致NO传感器读数不匹配。由于存在气态氨，因此NO传感器读数大约是实际NO浓度和氨浓度的组合。另一个现象是，氨气浓度对NO传感器读数的影响相对于不同温度有显着差异。基于这些观察和NO浓度读数的重要性，我们致力于开发一种算法来校正NO传感器读数。利用涵盖大多数典型SCR工作温度范围的大量实验数据，我们通过采用基于自适应网络的模糊推理系统（ANFIS）获得了交叉敏感度因子与温度之间的关系。然后将开发的模糊推理系统应用于实验验证测试。与现有的扩展卡尔曼滤波器（EKF）相比，所开发的模糊推理系统在阅读校正性能上具有突出的优势。

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