NOx Emission Prediction of Diesel Engine Based on GWO-LSTM
NOx emission of Diesel engine is the main harmful emission substance of motor vehicles;accurate measurement of NOx emission is conducive to the control of urea injection to reduce emissions.However,the existing NOx sensors and emis-sion MAP obtained by calibration are both difficult to achieve real-time measurement of NOx under transient conditions.Princi-pal component analysis(PCA)was used to reduce the dimension of diesel engine operating parameters for world harmonized transient cycle(WHTC).A real-time diesel NOx prediction model was built based on long and short-term memory(LSTM)neural network,and the parameters of LSTM were optimized by grey wolf optimization(GWO)algorithm.The results show that the mean absolute percentage error(MAPE)of GMO-LSTM prediction model on the untrained data set is 3.23%,which proves that the model can accurately achieve real-time prediction of NOx emissions of diesel engines.In addition,the model has good generalization ability and reliability,which provides a reference for the realization of diesel emission control with software instead of hardware.
diesel enginenitrogen oxideprediction modellong and short-term memory neural networkgrey wolf optimization algorithm
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