Abstract
For diesel engines, accurate prediction of NOx (Nitrogen Oxides) emission plays an essential role in virtual NOx sensor development and engine design under situations of actual road driving. However, due to the randomness and uncertainty in the driving process of diesel vehicles, it is difficult to make predictions about NOx emissions. In order to solve this problem, this paper proposes differential models for noise reductions of NOx emissions in time series. First, according to the internal fluctuation of time series, use SSA (Singular Spectrum Analysis) to reduce the noises of the original time series; second, use ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to decompose the noise-reducing data into several relatively stable subsequences; third, use the sample entropy to calculate the complexity of each subsequence, and divide the sequences into high-frequency ones and low-frequency ones; finally, use GRU (Gated Recurrent Unit) to complete the prediction of high-frequency sequences and SVR (Support Vector Regression) for the prediction of low-frequency sequences. To obtain the final models, integrate the prediction results of the subsequences. Make comparisons with five single models, SSA single-processing models, and ICEEMDAN single-processing models. The experimental results show that the proposed model can predict the instantaneous NOx emissions of diesel engines better than the single model and the model processed by SSA, and the differentiated model can effectively improve the execution speed of the model.
Highlights
The diesel vehicle is the main source of NOx emission
In order to test the abilities of SSA-ICEEMDAN-SVR-GRU models to predict the NOx emissions of diesel vehicles in actual roads, we compare the performance of 12 different prediction models, including SVR models, LSTM models, GRU models, SSA-SVR models, SSA-LSTM models, SSA-GRU models, ICEEMDAN-SVR models, ICEEMDAN-LSTM models, ICEEMDAN-GRU models, RF (Random Forest) models, and Bayes network models
GRU, LSTM, and SVR that have undergone SSA one-step noise reduction; in addition, ICEEMDAN-GRU, ICEEMDAN-LSTM, and ICEEMDAN-SVR models are added for comparison, involving a total of 12 different models for comparison
Summary
The diesel vehicle is the main source of NOx emission. In China, according to the 2019. In order to reduce emissions by adjusting engine component parameters, it is necessary to go through the engine test bench or actual driving. Some scholars have used the chassis dynamometer or the engine test bench to collect the engine working data and construct the models according to the data obtained in the steady-state. These studies have achieved ideal experimental results, but the data obtained under steady-state conditions cannot capture the transient behavior of the engine from part load to full load as well as the hysteresis effect of the engine under start, stop, or cold start conditions [2], which is often quite different from the emission data generated by diesel vehicles driving on the real road
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