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The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines

Author

Listed:
  • Ruomiao Yang

    (Power Machinery and Vehicular Engineering Institute, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Tianfang Xie

    (School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA)

  • Zhentao Liu

    (Power Machinery and Vehicular Engineering Institute, College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

The indicated mean effective pressure (IMEP) is a key parameter for measuring the power output of an internal combustion engine (ICE). This indicator can be used to locate the high efficiency regions of engines. Therefore, it makes sense to predict the IMEP based on the machine learning (ML) approaches. However, different ML models are applicable to different scenarios, so it is important to choose the right model for prediction. The objective of this paper was to compare three ML models’ (ANN, SVR, RF) predictive performance in forecasting IMEP indicator with the input parameters spark timing (ST), speed and load. A validated one-dimensional (1D) computational fluid dynamics (CFD) model was employed to provide 756 sets of data for the training, validation, and testing of the model. The results indicated that the random forest (RF) model had the worst prediction performance, and support vector regression (SVR) had a slightly better prediction performance than the artificial neural network (ANN), at least for the investigations in this study. Overall, the ANN and SVR models showed good predictive performance for IMEP, as the coefficient of determination (R 2 ) was close to unity, and the root mean squared error (RMSE) was close to zero. Whereas the overall prediction results of the RF model are acceptable, the RF model does not learn well for some internal engine laws.

Suggested Citation

  • Ruomiao Yang & Tianfang Xie & Zhentao Liu, 2022. "The Application of Machine Learning Methods to Predict the Power Output of Internal Combustion Engines," Energies, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3242-:d:804812
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    References listed on IDEAS

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    1. Liu, Jinlong & Dumitrescu, Cosmin E., 2019. "Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition," Applied Energy, Elsevier, vol. 248(C), pages 95-103.
    2. Liu, Jinlong & Huang, Qiao & Ulishney, Christopher & Dumitrescu, Cosmin E., 2021. "Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine," Applied Energy, Elsevier, vol. 300(C).
    3. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    4. Fu-Kang Ma & Jun Wang & Yao-Nan Feng & Yan-Gang Zhang & Tie-Xiong Su & Yi Zhang & Yu-Hang Liu, 2017. "Parameter Optimization on the Uniflow Scavenging System of an OP2S-GDI Engine Based on Indicated Mean Effective Pressure (IMEP)," Energies, MDPI, vol. 10(3), pages 1-20, March.
    5. Yan, Ziming & Gainey, Brian & Gohn, James & Hariharan, Deivanayagam & Saputo, John & Schmidt, Carl & Caliari, Felipe & Sampath, Sanjay & Lawler, Benjamin, 2021. "A comprehensive experimental investigation of low-temperature combustion with thick thermal barrier coatings," Energy, Elsevier, vol. 222(C).
    6. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.
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    1. Hosseini, M. & Chitsaz, I., 2023. "Knock probability determination employing convolutional neural network and IGTD algorithm," Energy, Elsevier, vol. 284(C).

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