IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i18p6587-d910435.html
   My bibliography  Save this article

Spark Ignition Engine Modeling Using Optimized Artificial Neural Network

Author

Listed:
  • Hilkija Gaïus Tosso

    (Department of Electronics, Universidade Tecnológica Federal do Paraná-Ponta Grossa, Ponta Grossa 84017-220, PR, Brazil)

  • Saulo Anderson Bibiano Jardim

    (Powertrain Calibration, Renault do Brasil, São José dos Pinhas 83070-900, PR, Brazil)

  • Rafael Bloise

    (Powertrain Calibration, Renault do Brasil, São José dos Pinhas 83070-900, PR, Brazil)

  • Max Mauro Dias Santos

    (Department of Electronics, Universidade Tecnológica Federal do Paraná-Ponta Grossa, Ponta Grossa 84017-220, PR, Brazil)

Abstract

The spark ignition engine is a complex multi-domain system that contains many variables to be controlled and managed with the aim of attending to performance requirements. The traditional method and workflow of the engine calibration comprise measure and calibration through the design of an experimental process that demands high time and costs on bench testing. For the growing use of virtualization through artificial neural networks for physical systems at the component and system level, we came up with a likely efficiency adoption of the same approach for the case of engine calibration that could bring much better cost reduction and efficiency. Therefore, we developed a workflow integrated into the development cycle that allows us to model an engine black-box model based on an auto-generated feedfoward Artificial Neural Network without needing the human expertise required by a hand-crafted process. The model’s structure and parameters are determined and optimized by a genetic algorithm. The proposed method was used to create an ANN model for injection parameters calibration purposes. The experimental results indicated that the method could reduce the time and costs of bench testing.

Suggested Citation

  • Hilkija Gaïus Tosso & Saulo Anderson Bibiano Jardim & Rafael Bloise & Max Mauro Dias Santos, 2022. "Spark Ignition Engine Modeling Using Optimized Artificial Neural Network," Energies, MDPI, vol. 15(18), pages 1-23, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6587-:d:910435
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/18/6587/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/18/6587/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohd Nadhir Ab Wahab & Samia Nefti-Meziani & Adham Atyabi, 2015. "A Comprehensive Review of Swarm Optimization Algorithms," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-36, May.
    2. Ramadhas, A.S. & Jayaraj, S. & Muraleedharan, C., 2006. "Theoretical modeling and experimental studies on biodiesel-fueled engine," Renewable Energy, Elsevier, vol. 31(11), pages 1813-1826.
    3. 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.
    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. Youcef Sehili & Khaled Loubar & Lyes Tarabet & Mahfoudh Cerdoun & Clément Lacroix, 2024. "Computational Investigation of the Influence of Combustion Chamber Characteristics on a Heavy-Duty Ammonia Diesel Dual Fuel Engine," Energies, MDPI, vol. 17(5), pages 1-19, March.

    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. Haseeb Yaqoob & Yew Heng Teoh & Farooq Sher & Muhammad Umer Farooq & Muhammad Ahmad Jamil & Zareena Kausar & Noor Us Sabah & Muhammad Faizan Shah & Hafiz Zia Ur Rehman & Atiq Ur Rehman, 2021. "Potential of Waste Cooking Oil Biodiesel as Renewable Fuel in Combustion Engines: A Review," Energies, MDPI, vol. 14(9), pages 1-20, April.
    2. Sangeeta & Kapil Sharma & Manju Bala, 2020. "An ecological space based hybrid swarm-evolutionary algorithm for software reliability model parameter estimation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 77-92, February.
    3. Hormozi, Elham & Hu, Shuwen & Ding, Zhe & Tian, Yu-Chu & Wang, You-Gan & Yu, Zu-Guo & Zhang, Weizhe, 2022. "Energy-efficient virtual machine placement in data centres via an accelerated Genetic Algorithm with improved fitness computation," Energy, Elsevier, vol. 252(C).
    4. Talebian-Kiakalaieh, Amin & Amin, Nor Aishah Saidina & Zarei, Alireza & Noshadi, Iman, 2013. "Transesterification of waste cooking oil by heteropoly acid (HPA) catalyst: Optimization and kinetic model," Applied Energy, Elsevier, vol. 102(C), pages 283-292.
    5. 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.
    6. Minfang Huang & Qiong Guo & Jing Liu & Xiaoxu Huang, 2018. "Mixed Model Assembly Line Scheduling Approach to Order Picking Problem in Online Supermarkets," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    7. Himansu Das & Sanjay Prajapati & Mahendra Kumar Gourisaria & Radha Mohan Pattanayak & Abdalla Alameen & Manjur Kolhar, 2023. "Feature Selection Using Golden Jackal Optimization for Software Fault Prediction," Mathematics, MDPI, vol. 11(11), pages 1-28, May.
    8. Kharkeshi, Behrad Alizadeh & Shafaghat, Rouzbeh & Jahanian, Omid & Alamian, Rezvan & Rezanejad, Kourosh, 2022. "Experimental study of an oscillating water column converter to optimize nonlinear PTO using genetic algorithm," Energy, Elsevier, vol. 260(C).
    9. Khamis, Nurulaqilla & Selamat, Hazlina & Ismail, Fatimah Sham & Lutfy, Omar Farouq & Haniff, Mohamad Fadzli & Nordin, Ili Najaa Aimi Mohd, 2020. "Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization," Chaos, Solitons & Fractals, Elsevier, vol. 131(C).
    10. Mustafa Erkan Turan, 2016. "Fuzzy Systems Tuned By Swarm Based Optimization Algorithms for Predicting Stream flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4345-4362, September.
    11. M, Vinod Babu & K, Madhu Murthy & G, Amba Prasad Rao, 2017. "Butanol and pentanol: The promising biofuels for CI engines – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1068-1088.
    12. Yang, Fubin & Zhang, Hongguang & Hou, Xiaochen & Tian, Yaming & Xu, Yonghong, 2019. "Experimental study and artificial neural network based prediction of a free piston expander-linear generator for small scale organic Rankine cycle," Energy, Elsevier, vol. 175(C), pages 630-644.
    13. Afroz Alam & Preeti Verma & Mohd Tariq & Adil Sarwar & Basem Alamri & Noore Zahra & Shabana Urooj, 2021. "Jellyfish Search Optimization Algorithm for MPP Tracking of PV System," Sustainability, MDPI, vol. 13(21), pages 1-20, October.
    14. Ganapathy, T. & Murugesan, K. & Gakkhar, R.P., 2009. "Performance optimization of Jatropha biodiesel engine model using Taguchi approach," Applied Energy, Elsevier, vol. 86(11), pages 2476-2486, November.
    15. 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.
    16. Mohammad Javad Amoshahy & Mousa Shamsi & Mohammad Hossein Sedaaghi, 2016. "A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-42, August.
    17. Rehan Ali Khan & Shiyou Yang & Shafiullah Khan & Shah Fahad & Kalimullah, 2021. "A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices," Energies, MDPI, vol. 14(24), pages 1-19, December.
    18. Shafiq Ahmad, 2022. "Electromagnetic Field Optimization Based Selective Harmonic Elimination in a Cascaded Symmetric H-Bridge Inverter," Energies, MDPI, vol. 15(20), pages 1-18, October.
    19. Saleh, H.E., 2009. "Effect of exhaust gas recirculation on diesel engine nitrogen oxide reduction operating with jojoba methyl ester," Renewable Energy, Elsevier, vol. 34(10), pages 2178-2186.
    20. Memon, Mudasir Ahmed & Mekhilef, Saad & Mubin, Marizan & Aamir, Muhammad, 2018. "Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2235-2253.

    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:gam:jeners:v:15:y:2022:i:18:p:6587-:d:910435. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.