IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v283y2021ics0306261920316858.html
   My bibliography  Save this article

Detection of transient low-temperature combustion characteristics by ion current – The missing link for homogeneous charge compression ignition control?

Author

Listed:
  • Wang, Jinqiu
  • Bedei, Julian
  • Deng, Jun
  • Andert, Jakob
  • Zhu, Denghao
  • Li, Liguang

Abstract

Homogeneous charge compression ignition is a promising low-temperature combustion mode because of its high efficiency and low emissions; however, its strong cycle-to-cycle coupling effect, which caused by the recirculation of exhaust gases, may entail problems with low combustion stability. In this study, a new concept that extracts more comprehensive combustion information in homogeneous charge compression ignition is proposed through the integration of ion current and in-cylinder pressure sensing. To analyze the correlations of combustion parameters and their relationships with the ion current parameters, steady-state measurements were conducted. Dynamic measurements were implemented to form a comprehensive database for artificial neural network training. To investigate the hypothesis that the ion current gives additional information beyond the pressure trace, black-box models based on experimental data are trained. The results show that the baseline model trained purely with the manipulated variables has the worst performance, while the model including both in-cylinder pressure and ion current derived parameters has the best predictability, with the overall root-mean-square error reduced by 2.5% in predicting combustion phasing, compared with in-cylinder pressure based model. It demonstrates that a significant improvement in model quality can be achieved by the combination of ion current and in-cylinder pressure sensing, which indicates that the ion current signal contains information that goes beyond a sole analysis of the pressure trace. By complementing the in-cylinder pressure, the use of the ion current as a “chemical sensor” for low-temperature combustion thus appears very promising for the stable control of homogeneous charge compression ignition combustion.

Suggested Citation

  • Wang, Jinqiu & Bedei, Julian & Deng, Jun & Andert, Jakob & Zhu, Denghao & Li, Liguang, 2021. "Detection of transient low-temperature combustion characteristics by ion current – The missing link for homogeneous charge compression ignition control?," Applied Energy, Elsevier, vol. 283(C).
  • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316858
    DOI: 10.1016/j.apenergy.2020.116299
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920316858
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2020.116299?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. Wick, Maximilian & Bedei, Julian & Andert, Jakob & Lehrheuer, Bastian & Pischinger, Stefan & Nuss, Eugen, 2020. "Dynamic measurement of HCCI combustion with self-learning of experimental space limitations," Applied Energy, Elsevier, vol. 262(C).
    2. Wick, Maximilian & Bedei, Julian & Gordon, David & Wouters, Christian & Lehrheuer, Bastian & Nuss, Eugen & Andert, Jakob & Koch, Charles Robert, 2019. "In-cycle control for stabilization of homogeneous charge compression ignition combustion using direct water injection," Applied Energy, Elsevier, vol. 240(C), pages 1061-1074.
    3. Rezaei, Javad & Shahbakhti, Mahdi & Bahri, Bahram & Aziz, Azhar Abdul, 2015. "Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks," Applied Energy, Elsevier, vol. 138(C), pages 460-473.
    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. Baek, Seungju & Lee, Sanguk & Shin, Myunghwan & Lee, Jongtae & Lee, Kihyung, 2022. "Analysis of combustion and exhaust characteristics according to changes in the propane content of LPG," Energy, Elsevier, vol. 239(PC).
    2. Wu, Jingtao & Zhang, Zhehao & Kang, Zhe & Deng, Jun & Li, Liguang & Wu, Zhijun, 2022. "An assessment methodology for fuel/water consumption co-optimization of a gasoline engine with port water injection," Applied Energy, Elsevier, vol. 310(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. Denghao Zhu & Jun Deng & Jinqiu Wang & Shuo Wang & Hongyu Zhang & Jakob Andert & Liguang Li, 2020. "Development and Application of Ion Current/Cylinder Pressure Cooperative Combustion Diagnosis and Control System," Energies, MDPI, vol. 13(21), pages 1-21, October.
    2. Bahri, Bahram & Shahbakhti, Mahdi & Aziz, Azhar Abdul, 2017. "Real-time modeling of ringing in HCCI engines using artificial neural networks," Energy, Elsevier, vol. 125(C), pages 509-518.
    3. Desantes, J.M. & García-Oliver, J.M. & Vera-Tudela, W. & López-Pintor, D. & Schneider, B. & Boulouchos, K., 2016. "Study of the auto-ignition phenomenon of PRFs under HCCI conditions in a RCEM by means of spectroscopy," Applied Energy, Elsevier, vol. 179(C), pages 389-400.
    4. Ayhan, Vezir & Ece, Yılmaz Mert, 2020. "New application to reduce NOx emissions of diesel engines: Electronically controlled direct water injection at compression stroke," Applied Energy, Elsevier, vol. 260(C).
    5. Han, Zhiqiang & Li, Bolun & Tian, Wei & Xia, Qi & Leng, Songpeng, 2019. "Influence of coupling action of oxygenated fuel and gas circuit oxygen on hydrocarbons formation in diesel engine," Energy, Elsevier, vol. 173(C), pages 196-206.
    6. Cocco Mariani, Viviana & Hennings Och, Stephan & dos Santos Coelho, Leandro & Domingues, Eric, 2019. "Pressure prediction of a spark ignition single cylinder engine using optimized extreme learning machine models," Applied Energy, Elsevier, vol. 249(C), pages 204-221.
    7. Xiaohua Zeng & Haoyong Cui & Dafeng Song & Nannan Yang & Tong Liu & Huiyong Chen & Yinshu Wang & Yulong Lei, 2018. "Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model," Energies, MDPI, vol. 11(6), pages 1-20, June.
    8. Li, Yaopeng & Jia, Ming & Han, Xu & Bai, Xue-Song, 2021. "Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)," Energy, Elsevier, vol. 225(C).
    9. Thangarasu, Vinoth & M, Angkayarkan Vinayakaselvi & Ramanathan, Anand, 2021. "Artificial neural network approach for parametric investigation of biodiesel synthesis using biocatalyst and engine characteristics of diesel engine fuelled with Aegle Marmelos Correa biodiesel," Energy, Elsevier, vol. 230(C).
    10. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    11. Charalambides, A.G. & Sahu, S. & Hardalupas, Y. & Taylor, A.M.K.P. & Urata, Y., 2018. "Evaluation of Homogeneous Charge Compression Ignition (HCCI) autoignition development through chemiluminescence imaging and Proper Orthogonal Decomposition," Applied Energy, Elsevier, vol. 210(C), pages 288-302.
    12. Moradi, Jamshid & Gharehghani, Ayat & Mirsalim, Mostafa, 2020. "Numerical investigation on the effect of oxygen in combustion characteristics and to extend low load operating range of a natural-gas HCCI engine," Applied Energy, Elsevier, vol. 276(C).
    13. Geng, Zhiqiang & Li, Hongda & Zhu, Qunxiong & Han, Yongming, 2018. "Production prediction and energy-saving model based on Extreme Learning Machine integrated ISM-AHP: Application in complex chemical processes," Energy, Elsevier, vol. 160(C), pages 898-909.
    14. Hunicz, Jacek & Mikulski, Maciej & Koszałka, Grzegorz & Ignaciuk, Piotr, 2020. "Detailed analysis of combustion stability in a spark-assisted compression ignition engine under nearly stoichiometric and heavy EGR conditions," Applied Energy, Elsevier, vol. 280(C).
    15. Janakiraman, S. & Lakshmanan, T. & Raghu, P., 2021. "Experimental investigative analysis of ternary (diesel + biodiesel + bio-ethanol) fuel blended with metal-doped titanium oxide nanoadditives tested on a diesel engine," Energy, Elsevier, vol. 235(C).
    16. Wick, Maximilian & Bedei, Julian & Andert, Jakob & Lehrheuer, Bastian & Pischinger, Stefan & Nuss, Eugen, 2020. "Dynamic measurement of HCCI combustion with self-learning of experimental space limitations," Applied Energy, Elsevier, vol. 262(C).
    17. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    18. Jafarmadar, Samad & Nemati, Peyman, 2016. "Exergy analysis of diesel/biodiesel combustion in a homogenous charge compression ignition (HCCI) engine using three-dimensional model," Renewable Energy, Elsevier, vol. 99(C), pages 514-523.
    19. Bahri, Bahram & Shahbakhti, Mahdi & Kannan, Kaushik & Aziz, Azhar Abdul, 2016. "Identification of ringing operation for low temperature combustion engines," Applied Energy, Elsevier, vol. 171(C), pages 142-152.
    20. Neshat, Elaheh & Saray, Rahim Khoshbakhti & Hosseini, Vahid, 2016. "Effect of reformer gas blending on homogeneous charge compression ignition combustion of primary reference fuels using multi zone model and semi detailed chemical-kinetic mechanism," Applied Energy, Elsevier, vol. 179(C), pages 463-478.

    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:283:y:2021:i:c:s0306261920316858. 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.