Yang, Long (Beijing Institute of Technology), Huang, Ying (Beijing Institute of Technology), Xia, meng (Beijing Institute of Technology), Li, Hong (Beijing Institute of Technology)

Neural-Network Based Boost Pressure Prediction for Two-Stage Turbocharging System of Diesel Engine

Scheduled for presentation during the Regular Session "Control & Estimation II: Diesel engines" (MoBT3), Monday, June 24, 2019, 16:50−17:10, Chamerolles

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

A boost pressure prediction research based on neural network is carried out for a diesel engine with two-stage adjustable turbocharging system. First, based on the co-simulation using GT-POWER and MATLAB/Simulink model, the dynamic influence of cycle fuel injection quantity and bypass valve opening of the turbine on boost pressure of diesel engine is studied. The boost pressure shows strong nonlinear relationship with the two aforementioned affecting parameters. Second, a nonlinear autoregressive neural network prediction model (NARXNN) with external input is designed to predict the boost pressure of the diesel engine. The cycle fuel injection quantity, bypass valve opening of turbine, and current boost pressure are used as the inputs of the neural network in order to predict the future boost pressure. Then the neural network prediction model is identified based on the mean absolute percentage error (MAPE). The identification results show that the minimum MAPE (3.52%) is achieved when the number of hidden layer nodes is 15. At last, the neural network prediction model for boost pressure is verified by simulation. The one-step prediction simulation results of boost pressure show that the established prediction model can accurately predict the boost pressure, and the MAPE of the verification data is 3.98%. And the multistep prediction simulation results show that the pressure prediction error in the 50-step prediction domain is within 0.05bar, which indicates that the prediction model has high accuracy.