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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Vydané v:	Energy (Oxford) Ročník 262; s. 125262
Hlavní autori:	Li, Yangyang, Zhou, Shi, Liu, Jingping, Tong, Ji, Dang, Jian, Yang, Fuyuan, Ouyang, Minggao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.01.2023
Predmet:	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
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Abstract	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 (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA Ⅲ algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA Ⅲ fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO2 and CO (g/kW·h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multi-objective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model. •High-accuracy simulation-optimization platform for the engine is developed.•NSGA Ⅲ and SVM are coupled, clarified and applied for the optimization of full engine MAPs.•Maximum reduction rates of BSFC, NOx, and CO (g/kW·h) reach 7.07%, 35.90%, and 5.50%.•Time cost of the optimization method of SVM model is 1/23 of that for the physical model.
AbstractList	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 (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA Ⅲ algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA Ⅲ fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO₂ and CO (g/kW·h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multi-objective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model. 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 (intake), VVT-E (exhaust) and exhaust gas recirculation (EGR). The engine GT-Power physical model is used to generate training data for the SVM, and verify the accuracy of the results of NSGA Ⅲ algorithm during the optimization process. The SVM with fast calculation speed is used in the calculation of NSGA Ⅲ fitness evaluation. In addition, enhancing training is utilized to improve the accuracy of the SVM model in this research. When the optimization method is applied to the Atkinson cycle gasoline engine, its high efficiency has been presented. In the three plans obtained by GT-Power physical model with all four parameters optimized, the maximum reduction rates of BSFC, NOx, CO2 and CO (g/kW·h) reached 7.07%, 35.90%, 6.62% and 5.50% respectively. The SVM model is compared with back-propagation algorithm, and the result proves that SVM is more suitable for such problems. Finally, based on the Pareto optimal solution obtained by the optimization method, it significantly promotes the solution of multi-objective optimization problems. Theoretically, the time cost of the optimization method in this paper can reach 1/23 of that for the optimization algorithm directly driving physical model. •High-accuracy simulation-optimization platform for the engine is developed.•NSGA Ⅲ and SVM are coupled, clarified and applied for the optimization of full engine MAPs.•Maximum reduction rates of BSFC, NOx, and CO (g/kW·h) reach 7.07%, 35.90%, and 5.50%.•Time cost of the optimization method of SVM model is 1/23 of that for the physical model.
ArticleNumber	125262
Author	Ouyang, Minggao Zhou, Shi Yang, Fuyuan Li, Yangyang Liu, Jingping Dang, Jian Tong, Ji
Author_xml	– sequence: 1 givenname: Yangyang surname: Li fullname: Li, Yangyang email: lyy025930@163.com organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China – sequence: 2 givenname: Shi surname: Zhou fullname: Zhou, Shi email: 769536391@qq.com organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China – sequence: 3 givenname: Jingping surname: Liu fullname: Liu, Jingping email: l19620426@163.com organization: State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha, 410082, China – sequence: 4 givenname: Ji surname: Tong fullname: Tong, Ji organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China – sequence: 5 givenname: Jian surname: Dang fullname: Dang, Jian email: 3371486484@qq.com organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China – sequence: 6 givenname: Fuyuan surname: Yang fullname: Yang, Fuyuan organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China – sequence: 7 givenname: Minggao surname: Ouyang fullname: Ouyang, Minggao organization: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China
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Keywords	Atkinson cycle gasoline engine Digital twins Support vector machine algorithm Back-propagation algorithm NSGA Ⅲ algorithm Enhancing training
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Snippet	This paper presents an optimization method using Non-dominated Sorting Genetic Algorithm (NSGA) Ⅲ to drive support vector machine (SVM). In the NSGA Ⅲ...
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SubjectTerms	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
Title	Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA Ⅲ coupled with support vector machine and back-propagation algorithm
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