Modelling and simulation research of vehicle engines based on computational intelligence methods

摘要

We assess the feasibility of two kinds of widely used artificial neural network (ANN) technologies applied in the field of transient emission simulation. In this work, the back-propagation feedforward neural network (BPNN) is shown to be more suitable than the radial basis function neural network (RBFNN). Considering the transient change rule of a transient operation, the composite transient rate is innovatively adopted as an input variable to the BPNN transient emission model, which is composited by the torque transient rate and air-fuel ratio (AFR) transient rate. Thus, a whole process transient simulation platform based on the multi-soft coupling technology of a test diesel engine is established. Through a transient emission simulation, the veracity and generalisation ability of the simulation platform is confirmed. The simulation platform can correctly predict the change trends and establish a peak value difference within 8. Our findings suggest that the simulation platform can be applied to a study of the control strategies of typical transient operations.