Neural Network Model of Turbocharged Diesel Engine Based on Particle Swarm Algorithm

CHEN Haotian; WANG Yue; CAO Jing; ZHANG Jizhong; DENG Kangyao; CUI Yi

doi:10.3969/j.issn.1001-2222.2021.03.001

Vehicle Engine ›› 2021, Vol. 0 ›› Issue (3) : 1-7. DOI: 10.3969/j.issn.1001-2222.2021.03.001

Neural Network Model of Turbocharged Diesel Engine Based on Particle Swarm Algorithm

CHEN Haotian¹，WANG Yue²，CAO Jing³，ZHANG Jizhong³，DENG Kangyao¹，CUI Yi¹

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Abstract

Neural network is an effective method to establish a realtime simulation model of diesel engine performance. Most of the current research on diesel engine neural network models was carried out for steadystate conditions. In order to realize transient performance prediction of diesel engine, a construction method of general neural network model was proposed to predict steadystate and transient performance of diesel engine in all operating conditions. In addition, in order to solve the problem that the traditional backpropagation (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 steadystate 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 PSOBP neural network model can effectively predict the steadystate and transient performance of engine. The maximum error of steadystate and transient prediction is 4.54% and 4.93% respectively. Compared with the traditional BP neural network, PSOBP model can effectively realize global optimization and improve generalization ability.

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diesel engine / neural network / particle swarm algorithm / numerical model / generalization ability

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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 https://doi.org/10.3969/j.issn.1001-2222.2021.03.001