Most of the parameters needed to predict Nitrogen Oxides (NOx) emissions, for example, combustion temperature, oxygen concentration and in-cylinder composition ratio, can be predicted by phenomenological 0-D prediction model if accurate EGR rates are provided. However, it is difficult to predict the EGR rate itself accurately by the model, so the EGR rate is predicted by the temperature measurement method. Although this method predicts EGR rates very accurately and quickly, there are some problems such as thermocouple failures and the difficulty in applying to mass production engines, so it is necessary to predict EGR rates by another method. The deep learning method follows an inductive methodology that extracts common characteristics of data based on a lot of data themselves. Therefore, although it requires a lot of experimental data, it has an advantage of high accuracy that can be obtained without any feature engineering. In this study, the EGR rate, which was difficult to predict in the past, was predicted by making various models using the deep learning method. Finally, EGR rate was predicted with a high accuracy of R-square 0.9994 and root mean squared error 0.0692 using a deep learning method at 1500 rpm and bmep 4, 6 and 8 bar. This study can be used as a basic study to predict EGR rates in transient and RDE conditions.

中文翻译：

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深度学习方法在稳态工况下柴油机的EGR预测

如果提供了准确的EGR率，则可以通过现象学的0维预测模型来预测预测氮氧化物（NOx）排放所需的大多数参数，例如燃烧温度，氧气浓度和缸内组成比。然而，难以通过模型准确地预测EGR率本身，因此，通过温度测量方法来预测EGR率。尽管该方法可以非常准确和快速地预测EGR率，但是仍然存在一些问题，例如热电偶故障以及难以应用于量产发动机，因此有必要通过另一种方法来预测EGR率。深度学习方法遵循归纳方法，该方法基于大量数据本身提取数据的共同特征。因此，尽管需要大量实验数据，它具有无需任何特征工程就可以获得的高精度的优点。在这项研究中，通过使用深度学习方法制作各种模型来预测过去很难预测的EGR率。最后，使用深度学习方法在1500 rpm和bmep 4、6和8 bar下，以较高的R平方精度0.9994和均方根误差0.0692预测EGR率。该研究可以用作预测瞬态和RDE条件下EGR率的基础研究。0692使用1500 rpm和bmep 4、6和8 bar的深度学习方法。该研究可以用作预测瞬态和RDE条件下EGR率的基础研究。0692使用1500 rpm和bmep 4、6和8 bar的深度学习方法。该研究可以用作预测瞬态和RDE条件下EGR率的基础研究。