A new transient NOx prediction model for diesel engine based on neural network model with Pelican Optimization Algorithm

Abstract

The World Harmonized Steady Cycle (WHSC) and the World Harmonized Transient Cycle (WHTC) are used to simulate the real driving conditions of a vehicle, which provides an opportunity for researchers to predict engine emissions through the use of models and thus investigate engine emission technologies or technology optimization to meet regulations. In this study, a Back-Propagation neural network (BPNN) is used to build an engine NOx prediction model, determine the number of nodes in the hidden layer, and optimize its weights and thresholds using the Pelican Optimization Algorithm (POA). NOx emissions from diesel engines are predicted by using the WHTC. The results show that the proposed model has a high R coefficient value of 0.9641. The results of the cold WHTC cycle NOx emission prediction shows that the mean absolute error (MAE) for the three cycles is about 32–35 ppm. The MAE was approximately 2.8% of the maximum NOx emissions in all cycles. These results indicate that the model has high accuracy in predicting transient NOx emissions.