Spark Ignition Engine Modeling Using Optimized Artificial Neural Network

Hilkija Gaïus Tosso, Saulo Anderson Bibiano Jardim, Rafael Bloise and Max Mauro Dias Santos ()
Additional contact information
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

Energies, 2022, vol. 15, issue 18, 1-23

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.

Keywords: spark ignition engine; modeling; artificial neural network; genetic algorithm and optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/18/6587/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/18/6587/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:18:p:6587-:d:910435

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().