IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v310y2022ics0306261922000228.html
   My bibliography  Save this article

Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines

Author

Listed:
  • Li, Ji
  • Zhou, Quan
  • Williams, Huw
  • Xu, Pu
  • Xu, Hongming
  • Lu, Guoxiang

Abstract

The accurate characterization of volumetric efficiency is essential for modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To minimize experimental effort on sample collection and maintain high-precision volumetric efficiency characterization, this paper proposes a new methodology of fuzzy-tree-constructed data-efficient modelling to precisely quantify the air mass flow through the engine. Differing from conventional data-driven modelling, this methodology introduces a hierarchical fuzzy inference tree (HFIT) with three original topologies that accommodates simplicity by combining several low-dimensional fuzzy inference systems. Driven by two derivative-free optimization algorithms, a two-step tuning process is introduced to speed up the convergence process when traversing HFIT parameters. A Gaussian distributed resampling technique is developed to screen a small number of samples with diverse engine operations to maintain sample diversity. The experimental dataset is obtained from steady-state tests carried out in a BYD 1.5L gasoline engine specially made for a hybrid powertrain. The results demonstrate that the proposed fuzzy-tree-constructed data-efficient modelling methodology performs with superior learning efficiency on volumetric efficiency characterization than those of a fuzzy inference system, a neural network, or an adaptive neuro-fuzzy inference system. Even when dataset split ratio downs to 0.2, the relative mean absolute error can be restricted to 3.18% with the help of Gaussian distributed resampling technique.

Suggested Citation

  • Li, Ji & Zhou, Quan & Williams, Huw & Xu, Pu & Xu, Hongming & Lu, Guoxiang, 2022. "Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines," Applied Energy, Elsevier, vol. 310(C).
  • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000228
    DOI: 10.1016/j.apenergy.2022.118534
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922000228
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.118534?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. He, Yinglong & Wang, Chongming & Zhou, Quan & Li, Ji & Makridis, Michail & Williams, Huw & Lu, Guoxiang & Xu, Hongming, 2020. "Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system," Applied Energy, Elsevier, vol. 266(C).
    2. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    3. Cornolti, L. & Onorati, A. & Cerri, T. & Montenegro, G. & Piscaglia, F., 2013. "1D simulation of a turbocharged Diesel engine with comparison of short and long EGR route solutions," Applied Energy, Elsevier, vol. 111(C), pages 1-15.
    4. Bahiuddin, Irfan & Mazlan, Saiful Amri & Imaduddin, Fitrian & Ubaidillah,, 2017. "A new control-oriented transient model of variable geometry turbocharger," Energy, Elsevier, vol. 125(C), pages 297-312.
    5. Mezher, Haitham & Chalet, David & Migaud, Jérôme & Chesse, Pascal, 2013. "Frequency based approach for simulating pressure waves at the inlet of internal combustion engines using a parameterized model," Applied Energy, Elsevier, vol. 106(C), pages 275-286.
    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. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(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. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    2. Kumar, Thanikasalam & Mohsin, Rahmat & Majid, Zulkifli Abd. & Ghafir, Mohammad Fahmi Abdul & Wash, Ananth Manickam, 2020. "Experimental study of the anti-knock efficiency of high-octane fuels in spark ignited aircraft engine using response surface methodology," Applied Energy, Elsevier, vol. 259(C).
    3. Reihani, Amin & Hoard, John & Klinkert, Stefan & Kuan, Chih-Kuang & Styles, Daniel & McConville, Greg, 2020. "Experimental response surface study of the effects of low-pressure exhaust gas recirculation mixing on turbocharger compressor performance," Applied Energy, Elsevier, vol. 261(C).
    4. Andwari, Amin Mahmoudzadeh & Aziz, Azhar Abdul & Said, Mohd Farid Muhamad & Latiff, Zulkarnain Abdul, 2014. "Experimental investigation of the influence of internal and external EGR on the combustion characteristics of a controlled auto-ignition two-stroke cycle engine," Applied Energy, Elsevier, vol. 134(C), pages 1-10.
    5. Li, Ji & Wu, Dawei & Mohammadsami Attar, Hassan & Xu, Hongming, 2022. "Geometric neuro-fuzzy transfer learning for in-cylinder pressure modelling of a diesel engine fuelled with raw microalgae oil," Applied Energy, Elsevier, vol. 306(PA).
    6. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    7. Marco Bietresato & Carlo Caligiuri & Anna Bolla & Massimiliano Renzi & Fabrizio Mazzetto, 2019. "Proposal of a Predictive Mixed Experimental- Numerical Approach for Assessing the Performance of Farm Tractor Engines Fuelled with Diesel- Biodiesel-Bioethanol Blends," Energies, MDPI, vol. 12(12), pages 1-45, June.
    8. Kim, Dong-Min & Lee, Soo-Gyung & Kim, Dae-Kee & Park, Min-Ro & Lim, Myung-Seop, 2022. "Sizing and optimization process of hybrid electric propulsion system for heavy-duty vehicle based on Gaussian process modeling considering traction motor characteristics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    9. Zamboni, Giorgio & Moggia, Simone & Capobianco, Massimo, 2016. "Hybrid EGR and turbocharging systems control for low NOX and fuel consumption in an automotive diesel engine," Applied Energy, Elsevier, vol. 165(C), pages 839-848.
    10. Andrés Omar Tiseira Izaguirre & Roberto Navarro García & Lukas Benjamin Inhestern & Natalia Hervás Gómez, 2020. "Design and Numerical Analysis of Flow Characteristics in a Scaled Volute and Vaned Nozzle of Radial Turbocharger Turbines," Energies, MDPI, vol. 13(11), pages 1-19, June.
    11. Wenyu Gu & Wanhua Su, 2023. "Study on the Effects of Exhaust Gas Recirculation and Fuel Injection Strategy on Transient Process Performance of Diesel Engines," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    12. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Gaillard, Patrick, 2021. "Impact of the hybrid electric architecture on the performance and emissions of a delivery truck with a dual-fuel RCCI engine," Applied Energy, Elsevier, vol. 301(C).
    13. Kuo Jiang & Hong Zeng & Zefan Wu & Jianping Sun & Cai Chen & Bing Han, 2023. "Study on the Effect of Parameter Sensitivity on Engine Optimization Results," Energies, MDPI, vol. 16(23), pages 1-16, December.
    14. Deshmukh, Minal & Pathan, Aadil, 2024. "Bambusa tulda: A potential feedstock for bioethanol and its blending effects on the performance of spark ignition engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    15. Han, Zhiqiang & Ding, Jiawei & Du, Defeng & Tian, Wei & Wu, Xueshun & Xia, Qi & Zuo, Zinong, 2023. "Equivalent model-based optimal matching for two-stage turbocharging systems with bypass valves," Energy, Elsevier, vol. 264(C).
    16. Tauzia, Xavier & Maiboom, Alain & Karaky, Hassan, 2017. "Semi-physical models to assess the influence of CI engine calibration parameters on NOx and soot emissions," Applied Energy, Elsevier, vol. 208(C), pages 1505-1518.
    17. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    18. How, H.G. & Teoh, Y.H. & Krishnan, B. Navaneetha & Le, T.D. & Nguyen, H.T. & Prabhu, C., 2021. "Prediction of optimum Palm Oil Methyl Ester fuel blend for compression ignition engine using Response Surface Methodology," Energy, Elsevier, vol. 234(C).
    19. Serrano, José Ramón & Olmeda, Pablo & Arnau, Francisco J. & Dombrovsky, Artem & Smith, Les, 2015. "Turbocharger heat transfer and mechanical losses influence in predicting engines performance by using one-dimensional simulation codes," Energy, Elsevier, vol. 86(C), pages 204-218.
    20. Wang, Chongming & Xu, Hongming & Herreros, Jose Martin & Wang, Jianxin & Cracknell, Roger, 2014. "Impact of fuel and injection system on particle emissions from a GDI engine," Applied Energy, Elsevier, vol. 132(C), pages 178-191.

    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:appene:v:310:y:2022:i:c:s0306261922000228. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.