IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v248y2022ics0360544222002146.html
   My bibliography  Save this article

Use of thermodynamic models for compression ratio and peak firing pressure optimization in heavy-duty diesel engine

Author

Listed:
  • Bolu, Sencer
  • Ozgul, Emre
  • Epguzel, Emre
  • Gurel, Cetin

Abstract

European Union Commission declared 30% CO2 reduction target for 2019 report-out for heavy-duty vehicles. Nowadays, the main focus of automotive manufacturers is reducing CO2 emissions. To ensure 30% CO2 emission reduction, companies are examining the effect of different fuel economy contributors. Developing prototype diesel engines and performing tests for different conceptual studies are both expensive and complicated conditions. As a result, OEMs should employ virtual tools with an enhancing trend. In this paper, the fuel economy improvement effects of increasing the compression ratio and maximum in-cylinder pressure limitation in the 12.7L heavy-duty diesel engine are examined using a thermodynamic model rather than actual dynamometer tests. Initially, the generated thermodynamic model is correlated with dynamometer test data. A predictive combustion model is used. NOx emission prediction correlation is also performed. Then, the most vital operating points representing VECTO and on-road fuel economy cycles are detected. At these critical operating points, the thermodynamic model is run with varied calibration parameters such as fuel injection quantity & timing, boost pressure & air mass flow set points. Thermodynamic model outputs are used for the Gaussian Process Model generation. A multi-objective optimization methodology is utilized to thoroughly examine the fuel consumption and NOx emission trade-off trends for various peak firing pressure hardware limit and compression ratios scenarios. It is proven that since the fuel residency of part load points is more critical than full load points in the VECTO cycle and long haul vehicles, the CO2 reduction potential of increasing compression ratio is observed to be comparatively lower concerning increasing peak firing pressure limit. The methodology can be utilized for performing fast and accurate fuel economy benefits examination of different technologies or concepts in a virtual platform resulting in a significant reduction in the number of actual dynamometer tests.

Suggested Citation

  • Bolu, Sencer & Ozgul, Emre & Epguzel, Emre & Gurel, Cetin, 2022. "Use of thermodynamic models for compression ratio and peak firing pressure optimization in heavy-duty diesel engine," Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222002146
    DOI: 10.1016/j.energy.2022.123311
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222002146
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123311?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. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar & Debroy, Durbadal, 2018. "Performance-exhaust emission prediction of diesosenol fueled diesel engine: An ANN coupled MORSM based optimization," Energy, Elsevier, vol. 153(C), pages 212-222.
    3. Roberto Finesso & Gilles Hardy & Claudio Maino & Omar Marello & Ezio Spessa, 2017. "A New Control-Oriented Semi-Empirical Approach to Predict Engine-Out NOx Emissions in a Euro VI 3.0 L Diesel Engine," Energies, MDPI, vol. 10(12), pages 1-26, November.
    4. Rakopoulos, Constantine D. & Rakopoulos, Dimitrios C. & Mavropoulos, George C. & Kosmadakis, George M., 2018. "Investigating the EGR rate and temperature impact on diesel engine combustion and emissions under various injection timings and loads by comprehensive two-zone modeling," Energy, Elsevier, vol. 157(C), pages 990-1014.
    Full references (including those not matched with items on IDEAS)

    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. Bhowmik, Subrata & Paul, Abhishek & Panua, Rajsekhar & Ghosh, Subrata Kumar, 2020. "Performance, combustion and emission characteristics of a diesel engine fueled with diesel-kerosene-ethanol: A multi-objective optimization study," Energy, Elsevier, vol. 211(C).
    2. 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).
    3. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    4. Yang, Xiao & He, Zhihong & Qiu, Penghua & Dong, Shikui & Tan, Heping, 2019. "Numerical investigations on combustion and emission characteristics of a novel elliptical jet-stabilized model combustor," Energy, Elsevier, vol. 170(C), pages 1082-1097.
    5. Theodoros C. Zannis & John S. Katsanis & Georgios P. Christopoulos & Elias A. Yfantis & Roussos G. Papagiannakis & Efthimios G. Pariotis & Dimitrios C. Rakopoulos & Constantine D. Rakopoulos & Athanas, 2022. "Marine Exhaust Gas Treatment Systems for Compliance with the IMO 2020 Global Sulfur Cap and Tier III NO x Limits: A Review," Energies, MDPI, vol. 15(10), pages 1-49, May.
    6. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    7. Bowen Zheng & Zhenghe Song & Enrong Mao & Quan Zhou & Zhenhao Luo & Zhichao Deng & Xuedong Shao & Yuxi Liu, 2022. "An ANN-PSO-Based Method for Optimizing Agricultural Tractors in Field Operation for Emission Reduction," Agriculture, MDPI, vol. 12(9), pages 1-16, August.
    8. Xie, Mingjiang & Zhao, Jianli & Zuo, Ming J. & Tian, Zhigang & Liu, Libin & Wu, Jinming, 2023. "Multi-objective maintenance decision-making of corroded parallel pipeline systems," Applied Energy, Elsevier, vol. 351(C).
    9. Zhang, Xuan-Kai & He, Ya-Ling & Li, Meng-Jie & Hu, Xin, 2022. "The study of heat-mass transfer characteristics and multi-objective optimization on electric arc furnace," Applied Energy, Elsevier, vol. 317(C).
    10. Zhang, Han & Gao, Xueping & Sun, Bowen & Qin, Zixue & Zhu, Hongtao, 2020. "Parameter analysis and performance optimization for the vertical pipe intake-outlet of a pumped hydro energy storage station," Renewable Energy, Elsevier, vol. 162(C), pages 1499-1518.
    11. Mokhtar, Maizura & Burns, Stephen & Ross, Dave & Hunt, Ian, 2017. "Exploring multi-objective trade-offs in the design space of a waste heat recovery system," Applied Energy, Elsevier, vol. 195(C), pages 114-124.
    12. Haruki Tajima & Takuya Tomidokoro & Takeshi Yokomori, 2022. "Deep Learning for Knock Occurrence Prediction in SI Engines," Energies, MDPI, vol. 15(24), pages 1-14, December.
    13. Rakopoulos, Dimitrios C. & Rakopoulos, Constantine D. & Kosmadakis, George M. & Mavropoulos, George C., 2024. "Assessing the cyclic variability of combustion and NO emissions in hydrogen-methane fueled HSSI engine via quasi-dimensional modeling under the influence of flame-kernel turbulence and equivalence rat," Energy, Elsevier, vol. 288(C).
    14. Rakopoulos, Constantine D. & Rakopoulos, Dimitrios C. & Kosmadakis, George M. & Papagiannakis, Roussos G., 2019. "Experimental comparative assessment of butanol or ethanol diesel-fuel extenders impact on combustion features, cyclic irregularity, and regulated emissions balance in heavy-duty diesel engine," Energy, Elsevier, vol. 174(C), pages 1145-1157.
    15. 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.
    16. Wei, Li & Yan, Fuwu & Hu, Jie & Xi, Guangwei & Liu, Bo & Zeng, Jiawei, 2017. "Nox conversion efficiency optimization based on NSGA-II and state-feedback nonlinear model predictive control of selective catalytic reduction system in diesel engine," Applied Energy, Elsevier, vol. 206(C), pages 959-971.
    17. Dey, Suman & Reang, Narath Moni & Majumder, Arindam & Deb, Madhujit & Das, Pankaj Kumar, 2020. "A hybrid ANN-Fuzzy approach for optimization of engine operating parameters of a CI engine fueled with diesel-palm biodiesel-ethanol blend," Energy, Elsevier, vol. 202(C).
    18. 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.
    19. Philip Cammin & Jingjing Yu & Stefan Voß, 2023. "Tiered prediction models for port vessel emissions inventories," Flexible Services and Manufacturing Journal, Springer, vol. 35(1), pages 142-169, March.
    20. Zhou, Yanlai & Guo, Shenglian & Chang, Fi-John & Xu, Chong-Yu, 2018. "Boosting hydropower output of mega cascade reservoirs using an evolutionary algorithm with successive approximation," Applied Energy, Elsevier, vol. 228(C), pages 1726-1739.

    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:248:y:2022:i:c:s0360544222002146. 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.