Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA Ⅲ coupled with support vector machine and back-propagation algorithm

This paper presents an optimization method using Non-dominated Sorting Genetic Algorithm (NSGA) Ⅲ to drive support vector machine (SVM). In the NSGA Ⅲ algorithm, brake specific fuel consumption (BSFC), NOx and CO2 are optimized by changing the engine control parameters including spark angle, VVT-I (...

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Vydáno v:	Energy (Oxford) Ročník 262; s. 125262
Hlavní autoři:	Li, Yangyang, Zhou, Shi, Liu, Jingping, Tong, Ji, Dang, Jian, Yang, Fuyuan, Ouyang, Minggao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.01.2023
Témata:	Atkinson cycle gasoline engine Back-propagation algorithm carbon dioxide Digital twins energy energy use and consumption Enhancing training gasoline engines NSGA Ⅲ algorithm Support vector machine algorithm support vector machines system optimization Atkinson cycle gasoline engine Digital twins Support vector machine algorithm Back-propagation algorithm NSGA Ⅲ algorithm Enhancing training
ISSN:	0360-5442
On-line přístup:	Získat plný text
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