Abstract
Accurate and stable prediction of NOx emissions from diesel vehicles plays a crucial role in the establishment of virtual NOx sensors and the development and design of diesel engines. This paper presents a method for estimating transient NOx emissions by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a long- and short-term memory neural network (LSTM). First, the CEEMDAN algorithm is used to reduce the non-stationarity and volatility of the transient NOx emission data to obtain multiple subseries with different frequencies. Secondly, a predictive model is developed for each subsequence using an LSTM neural network. Finally, the results of each subsequence prediction are summed to obtain the final prediction. The proposed model uses NOx emission data generated by an EU IV diesel bus during real road driving. The results show that (1) The use of CEEMDAN can effectively improve the smoothness of NOx transient emission data, as well as facilitate more effective extraction of internal characteristics and variations of the raw data. (2) LSTM has better learning and prediction capability for transient changes in NOx emissions. (3) The results of CEEMDAN-LSTM for RMSE, R2, MAE and NRMSE are 46.11,0.98, 29.82 and 2.71, respectively, which are better than the other model with improved prediction performance.
Highlights
Diesel engines offer high fuel economy and thermal efficiency and are widely used in heavy vehicles and Non-road machinery
In order to improve the NOx transient emission prediction performance of diesel vehicles, a method for estimating diesel vehicle transient NOx emissions based on the combination of signal processing CEEMDAN algorithm and long- and short-term memory neural network (LSTM) neural network is proposed
1) Among the models covered in the paper, the LSTM model using deep learning algorithms has better prediction performance
Summary
Diesel engines offer high fuel economy and thermal efficiency and are widely used in heavy vehicles and Non-road machinery. Compared to physical and chemical relationship models, machine learning can quickly establish non-linear relationships between diesel vehicle operating parameters and emissions, and has been widely used in the engineering field [12]. Lotfan et al [16] investigated engine speed, intake temperature, and output power as variable inputs to obtain optimal values for NOx emissions using artificial neural network and non-dominated sequencing genetic algorithm (NSGA-II). In the previously conducted studies, few scholars have used deep neural networks to develop diesel engine NOx emission prediction models. A model combining CEEMDAN and LSTM for predicting NOx emissions from diesel engines is proposed to effectively address the non-stationarity and non-linearity of real road emission data. The contributions are as follows: 1) Application of the decomposition algorithm CEEMDAN to the instantaneous prediction of diesel NOx to reduce the non-stationarity and complexity of the raw data. At this point the sequence is decomposed into k IMF and an R(n)
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