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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
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    References listed on IDEAS

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    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.
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