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Development of Engine Efficiency Characteristic in Dynamic Working States

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  • Piotr Bera

    (Department of Machine Design and Technology, Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

The objective of this paper is to present a new approach to the problem of combustion engine efficiency characteristic development in dynamic working states. The artificial neural network (ANN) method was used to build a mathematical model of the engine comprising the following parameters: Engine speed, angular acceleration, engine torque, torque change intensity, and fuel mass flow, measured on a test bed on a spark ignition engine in static and dynamic working states. A detailed analysis of ANN design, data preparation, the training method, and the ANN model accuracy are described. The paper presents conducted calculations that clearly show the suitability of the approach in every aspect. Then, a simplified ANN was created, which allows a two dimensional characteristic in dynamic states, including 4 variables, to be determined.

Suggested Citation

  • Piotr Bera, 2019. "Development of Engine Efficiency Characteristic in Dynamic Working States," Energies, MDPI, vol. 12(15), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:15:p:2906-:d:252493
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    References listed on IDEAS

    as
    1. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    2. Xixue Liu & Datong Qin & Shaoqian Wang, 2019. "Minimum Energy Management Strategy of Equivalent Fuel Consumption of Hybrid Electric Vehicle Based on Improved Global Optimization Equivalent Factor," Energies, MDPI, vol. 12(11), pages 1-17, May.
    3. Farzad Jaliliantabar & Barat Ghobadian & Gholamhassan Najafi & Talal Yusaf, 2018. "Artificial Neural Network Modeling and Sensitivity Analysis of Performance and Emissions in a Compression Ignition Engine Using Biodiesel Fuel," Energies, MDPI, vol. 11(9), pages 1-24, September.
    4. Guang, Hao & Jin, Hui, 2019. "Fuel consumption model optimization based on transient correction," Energy, Elsevier, vol. 169(C), pages 508-514.
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