Deep-learning models were developed and evaluated for predicting the engine-out emission of NO_x—one of the main pollutants emitted from diesel engines—under transient conditions, that is, the Worldwide Harmonized Light Vehicles Test Procedure (WLTP). Phenomena of transient conditions are difficult to predict accurately via the conventional modeling approaches. Two algorithms—the deep neural network (DNN) and long short-term memory (LSTM)—were evaluated regarding the accuracy and calculation time. Training was performed using measured data, and the results indicated that the LSTM model (R² = 0.9777, RMSE = 20.6 ppm) was more accurate than the DNN model (R² = 0.9671, RMSE = 25.5 ppm). However, the DNN model had a significantly higher computation speed (0.36 s) than the LSTM model (1381.0 s). Data preprocessing was performed to insert time-related information into the data; the DNN model trained with the measured data lacked this feature. Data were preprocessed by the calculation of the weighted average of previous timestep data to the current-timestep data. The weighted average was calculated according to various ratios, for example, 7:3, 6:4, 5:5, 4:6, and 3:7. By applying a 7:3 weighted average for training the DNN model, the accuracy of the DNN model was achieved to an R² value of 0.9741, and RMSE 22.8 ppm (only R² value 0.0036 smaller and RMSE 2.2 ppm larger than the LSTM model) without sacrificing the calculation speed. The results of this study suggest that data preprocessing of the DNN model is an effective method for achieving accuracy as high as that of the LSTM model. The developed DNN model for the NO_x emission prediction can be used as a virtual sensor for real-time prediction owing to its accuracy and computation time.

深学习模型进行了开发和评估用于预测NO的发动机排出的排放_X从柴油机发出的主要污染物-酮发动机-下瞬态条件下，即，全球统一轻型车辆测试程序（WLTP）。通过传统的建模方法很难准确地预测瞬态条件的现象。评估了两种算法-深度神经网络（DNN）和长短期记忆（LSTM）-的准确性和计算时间。使用实测数据进行训练，结果表明LSTM模型（R ²  = 0.9777，RMSE = 20.6 ppm）比DNN模型（R ² = 0.9671，RMSE = 25.5 ppm）。但是，DNN模型的计算速度（0.36 s）比LSTM模型（1381.0 s）高得多。进行了数据预处理，以将与时间相关的信息插入到数据中；用实测数据训练的DNN模型缺少此功能。通过计算先前时间步长数据对当前时间步长数据的加权平均值，对数据进行预处理。根据各种比率，例如7：3、6：4、5：5、4：6和3：7，计算加权平均值。通过将7：3加权平均值用于训练DNN模型，DNN模型的精度达到R ²值为0.9741和RMSE 22.8 ppm（仅R ²值比LSTM模型小0.0036，而RMSE比LSTM模型大2.2 ppm），而不会牺牲计算速度。这项研究的结果表明，DNN模型的数据预处理是一种实现与LSTM模型一样高的精度的有效方法。所开发的模型DNN为NO _X排放预测可以用作实时预测由于其精度和计算时间的虚拟传感器。