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Accurate and stable prediction of NO x emissions from diesel vehicles plays a crucial role in the establishment of virtual NO x sensors and the development and design of diesel engines. This paper presents a method for estimating transient NO x 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 NO x 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 NO x 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 NO x 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 NO x emissions. (3) The results of CEEMDAN-LSTM for RMSE, R 2 , 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.

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