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Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks

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  • Kara Togun, Necla
  • Baysec, Sedat

Abstract

This study presents an artificial neural network (ANN) model to predict the torque and brake specific fuel consumption of a gasoline engine. An explicit ANN based formulation is developed to predict torque and brake specific fuel consumption of a gasoline engine in terms of spark advance, throttle position and engine speed. The proposed ANN model is based on experimental results. Experimental studies were completed to obtain training and testing data. Of all 81 data sets, the training and testing sets consisted of randomly selected 63 and 18 sets, respectively. An ANN model based on a back-propagation learning algorithm for the engine was developed. The performance and an accuracy of the proposed ANN model are found satisfactory. This study demonstrates that ANN is very efficient for predicting the engine torque and brake specific fuel consumption. Moreover, the proposed ANN model is presented in explicit form as a mathematical function.

Suggested Citation

  • Kara Togun, Necla & Baysec, Sedat, 2010. "Prediction of torque and specific fuel consumption of a gasoline engine by using artificial neural networks," Applied Energy, Elsevier, vol. 87(1), pages 349-355, January.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:1:p:349-355
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    References listed on IDEAS

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    1. Gölcü, Mustafa & Sekmen, Yakup & ErduranlI, Perihan & Sahir Salman, M., 2005. "Artificial neural-network based modeling of variable valve-timing in a spark-ignition engine," Applied Energy, Elsevier, vol. 81(2), pages 187-197, June.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Arcaklioglu, Erol & Çelikten, Ismet, 2005. "A diesel engine's performance and exhaust emissions," Applied Energy, Elsevier, vol. 80(1), pages 11-22, January.
    4. Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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