IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v262y2023ipas0360544222021478.html
   My bibliography  Save this article

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

Author

Listed:
  • Li, Yangyang
  • Zhou, Shi
  • Liu, Jingping
  • Tong, Ji
  • Dang, Jian
  • Yang, Fuyuan
  • Ouyang, Minggao

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.

Suggested Citation

  • Li, Yangyang & Zhou, Shi & Liu, Jingping & Tong, Ji & Dang, Jian & Yang, Fuyuan & Ouyang, Minggao, 2023. "Multi-objective optimization of the Atkinson cycle gasoline engine using NSGA Ⅲ coupled with support vector machine and back-propagation algorithm," Energy, Elsevier, vol. 262(PA).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222021478
    DOI: 10.1016/j.energy.2022.125262
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222021478
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125262?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    2. Zhao, Jinxing & Xu, Min, 2013. "Fuel economy optimization of an Atkinson cycle engine using genetic algorithm," Applied Energy, Elsevier, vol. 105(C), pages 335-348.
    3. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    4. Bendu, Harisankar & Deepak, B.B.V.L. & Murugan, S., 2017. "Multi-objective optimization of ethanol fuelled HCCI engine performance using hybrid GRNN–PSO," Applied Energy, Elsevier, vol. 187(C), pages 601-611.
    5. Zhu, Zhenxia & Zhang, Fujun & Li, Changjiang & Wu, Taotao & Han, Kai & Lv, Jianguo & Li, Yunlong & Xiao, Xuelian, 2015. "Genetic algorithm optimization applied to the fuel supply parameters of diesel engines working at plateau," Applied Energy, Elsevier, vol. 157(C), pages 789-797.
    6. Zhao, Jinxing & Xu, Min & Li, Mian & Wang, Bin & Liu, Shuangzhai, 2012. "Design and optimization of an Atkinson cycle engine with the Artificial Neural Network Method," Applied Energy, Elsevier, vol. 92(C), pages 492-502.
    7. Kamiuto, K., 2006. "Comparison of basic gas cycles under the restriction of constant heat addition," Applied Energy, Elsevier, vol. 83(6), pages 583-593, June.
    8. Yang, Zhuyong & Miganakallu, Niranjan & Miller, Tyler & Bonfochi Vinhaes, Vinicius & Worm, Jeremy & Naber, Jeffrey & Roth, David, 2020. "Investigation of high load operation of spark-ignited over-expanded Atkinson cycle engine," Applied Energy, Elsevier, vol. 262(C).
    9. Liu, Jizhe & Zhang, Yuchen & Meng, Ke & Dong, Zhao Yang & Xu, Yan & Han, Siming, 2022. "Real-time emergency load shedding for power system transient stability control: A risk-averse deep learning method," Applied Energy, Elsevier, vol. 307(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ge, Yanlin & Wu, Heng & Chen, Lingen & Feng, Huijun & Xie, Zhihui, 2023. "Finite time and finite speed thermodynamic optimization for an irreversible Atkinson cycle," Energy, Elsevier, vol. 270(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Qi & Guo, Tao & Fu, Jianqin & Dai, Hongliang & Liu, Jingping, 2022. "Experimental study on the effects of injection parameters and exhaust gas recirculation on combustion, emission and performance of Atkinson cycle gasoline direct-injection engine," Energy, Elsevier, vol. 238(PB).
    2. De Bellis, Vincenzo, 2016. "Performance optimization of a spark-ignition turbocharged VVA engine under knock limited operation," Applied Energy, Elsevier, vol. 164(C), pages 162-174.
    3. Meng Xia & Fujun Zhang, 2020. "Application of Multi-Parameter Fuzzy Optimization to Enhance Performance of a Regulated Two-Stage Turbocharged Diesel Engine Operating at High Altitude," Energies, MDPI, vol. 13(17), pages 1-12, August.
    4. Tadros, M. & Ventura, M. & Guedes Soares, C., 2019. "Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine," Energy, Elsevier, vol. 168(C), pages 897-908.
    5. Ariel Alexi & Teddy Lazebnik & Labib Shami, 2024. "Microfounded Tax Revenue Forecast Model with Heterogeneous Population and Genetic Algorithm Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(5), pages 1705-1734, May.
    6. Zhao, Jinxing, 2017. "Research and application of over-expansion cycle (Atkinson and Miller) engines – A review," Applied Energy, Elsevier, vol. 185(P1), pages 300-319.
    7. Krishnamoorthi, M. & Malayalamurthi, R., 2018. "Engine characteristics analysis of chaulmoogra oil blends and corrosion analysis of injector nozzle using scanning electron microscopy/energy dispersive spectroscopy," Energy, Elsevier, vol. 165(PB), pages 1292-1319.
    8. Yi Dong & Jianmin Liu & Yanbin Liu & Xinyong Qiao & Xiaoming Zhang & Ying Jin & Shaoliang Zhang & Tianqi Wang & Qi Kang, 2020. "A RBFNN & GACMOO-Based Working State Optimization Control Study on Heavy-Duty Diesel Engine Working in Plateau Environment," Energies, MDPI, vol. 13(1), pages 1-24, January.
    9. Zhou, Xiaochuan & Wu, Gang & Wang, Chunyan & Zhang, Ruijun & Shi, Shuaipeng & Zhao, Wanzhong, 2024. "Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions," Energy, Elsevier, vol. 301(C).
    10. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    11. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    12. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    13. John Jairo Ceballos & Andrés Melgar & Francisco V. Tinaut, 2021. "Influence of Environmental Changes Due to Altitude on Performance, Fuel Consumption and Emissions of a Naturally Aspirated Diesel Engine," Energies, MDPI, vol. 14(17), pages 1-41, August.
    14. Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    15. Jing Tian & Dedi Liu & Shenglian Guo & Zhengke Pan & Xingjun Hong, 2019. "Impacts of Inter-Basin Water Transfer Projects on Optimal Water Resources Allocation in the Hanjiang River Basin, China," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    16. Tang, Qingsong & Yang, Yang & Luo, Chang & Yang, Zhong & Fu, Chunyun, 2022. "A novel electro-hydraulic compound braking system coordinated control strategy for a four-wheel-drive pure electric vehicle driven by dual motors," Energy, Elsevier, vol. 241(C).
    17. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    18. Sultana, U. & Khairuddin, Azhar B. & Sultana, Beenish & Rasheed, Nadia & Qazi, Sajid Hussain & Malik, Nimra Riaz, 2018. "Placement and sizing of multiple distributed generation and battery swapping stations using grasshopper optimizer algorithm," Energy, Elsevier, vol. 165(PA), pages 408-421.
    19. Ying Sun & Yuelin Gao & Xudong Shi, 2019. "Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity," Mathematics, MDPI, vol. 7(2), pages 1-16, February.
    20. Ren, Lei & Zhou, Sheng & Peng, Tianduo & Ou, Xunmin, 2022. "Greenhouse gas life cycle analysis of China's fuel cell medium- and heavy-duty trucks under segmented usage scenarios and vehicle types," Energy, Elsevier, vol. 249(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222021478. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.