Modelling and simulation research of vehicle engines based on computational intelligence methods
by Ling-ge Sui; Lan Huang
International Journal of Computational Science and Engineering (IJCSE), Vol. 18, No. 3, 2019

Abstract: 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.

Online publication date: Tue, 26-Mar-2019

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