Neural network is an effective method to establish a realtime simulation model of diesel engine performance. Most of the current research on diesel engine neural network models was carried out for steadystate conditions. In order to realize transient performance prediction of diesel engine, a construction method of general neural network model was proposed to predict steadystate and transient performance of diesel engine in all operating conditions. In addition, in order to solve the problem that the traditional backpropagation (BP) neural network could not guarantee the global optimal solution and the poor generalization ability, the particle swarm optimization algorithm (PSO) in the swarm intelligence algorithm was used for optimization. The steadystate and transient test data of a turbocharged diesel engine was used as samples to train the model and was compared with the traditional BP neural network model. The research results show that the PSOBP neural network model can effectively predict the steadystate and transient performance of engine. The maximum error of steadystate and transient prediction is 4.54% and 4.93% respectively. Compared with the traditional BP neural network, PSOBP model can effectively realize global optimization and improve generalization ability.
CHEN Haotian
,
WANG Yue
,
CAO Jing
,
ZHANG Jizhong
,
DENG Kangyao
,
CUI Yi
. Neural Network Model of Turbocharged Diesel Engine Based on Particle Swarm Algorithm[J]. Vehicle Engine, 2021
, 0(3)
: 1
-7
.
DOI: 10.3969/j.issn.1001-2222.2021.03.001
