Predicting transient diesel engine NOx emissions using time-series data preprocessing with deep-learning models

Abstract

Deep-learning models were developed and evaluated for predicting the engine-out emission of NOx—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 NOx emission prediction can be used as a virtual sensor for real-time prediction owing to its accuracy and computation time.