Torque Estimation and Closed-Loop Control of Parallel Hybrid Engine Using ANN Method

Abstract:

In this paper， a joint simulation model of GT-suite and MATLAB / Simulink was constructed by using the calibration data of the actual engine and a collaborative control module for estimated torque feedback based on intake air and engine state parameters was established. A comparison was made between the estimation results errors of the ANN method and the Map method for estimating the steady state， transient torque variation， upshift and downshift of the engine. The results show that the Map method is more reliable under steady-state conditions， and the error of ANN method is small at low， medium， and high engine speeds， with errors of 1.31%， 1.09%， and 1.52% lower than the ANN method， and the error of the ANN method is 5.62% and 1.32% lower than that of the Map method under torque transient conditions， and 1.93% and 0.84% lower than that of the Map method under lifting conditions.

Key words:hybrid engine;torque estimation;artificial neural network (ANN);Matlab/Simulink;GT-Suite;joint simulation