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

Authors: Ling-ge Sui; Lan Huang

Addresses: Computer Science and Technology Postdoctoral Flow Station, Jilin University, 130012 Changchun, Jilin, China; Academic Affairs Division, Changchun Finance College, 130022 Changchun, Jilin, China ' College of Computer Science and Technology, Jilin University, 130012 Changchun, Jilin, China

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.

Keywords: transient emission; simulation; back-propagation feedforward neural network; BPNN; radial basis function neural network; RBFNN; diesel engine.

DOI: 10.1504/IJCSE.2019.098533

International Journal of Computational Science and Engineering, 2019 Vol.18 No.3, pp.227 - 239

Received: 28 Jun 2016
Accepted: 31 Aug 2016

Published online: 26 Mar 2019 *

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