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Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine

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  • Togun, Necla
  • Baysec, Sedat

Abstract

This study presents genetic programming (GP) based model to predict the torque and brake specific fuel consumption a gasoline engine in terms of spark advance, throttle position and engine speed. The objective of this study is to develop an alternative robust formulations based on experimental data and to verify the use of GP for generating the formulations for gasoline engine torque and brake specific fuel consumption. 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. Considerable good performance was achieved in predicting gasoline engine torque and brake specific fuel consumption by using GP. The performance of accuracies of proposed GP models are quite satisfactory (R2Â =Â 0.9878 for gasoline engine torque and R2Â =Â 0.9744 for gasoline engine brake specific fuel consumption). The prediction of proposed GP models were compared to those of the neural network modeling, and strictly good agreement was observed between the two predictions. The proposed GP formulation is quite accurate, fast and practical.

Suggested Citation

  • Togun, Necla & Baysec, Sedat, 2010. "Genetic programming approach to predict torque and brake specific fuel consumption of a gasoline engine," Applied Energy, Elsevier, vol. 87(11), pages 3401-3408, November.
    • Handle: RePEc:eee:appene:v:87:y:2010:i:11:p:3401-3408
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    References listed on IDEAS

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    1. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa, 2010. "Optimization of capacity and operation for CCHP system by genetic algorithm," Applied Energy, Elsevier, vol. 87(4), pages 1325-1335, April.
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    3. 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.
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    Cited by:

    1. Duro, João A. & Ozturk, Umud Esat & Oara, Daniel C. & Salomon, Shaul & Lygoe, Robert J. & Burke, Richard & Purshouse, Robin C., 2023. "Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem," European Journal of Operational Research, Elsevier, vol. 307(1), pages 421-446.
    2. Yushan Yang & Nuoya Gong & Keying Xie & Qingfei Liu, 2022. "Predicting Gasoline Vehicle Fuel Consumption in Energy and Environmental Impact Based on Machine Learning and Multidimensional Big Data," Energies, MDPI, vol. 15(5), pages 1-17, February.
    3. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    4. Li, Yangtao & Khajepour, Amir & Devaud, Cécile & Liu, Kaimin, 2017. "Power and fuel economy optimizations of gasoline engines using hydraulic variable valve actuation system," Applied Energy, Elsevier, vol. 206(C), pages 577-593.
    5. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.
    6. Tadros, M. & Ventura, M. & Guedes Soares, C., 2019. "Optimization procedure to minimize fuel consumption of a four-stroke marine turbocharged diesel engine," Energy, Elsevier, vol. 168(C), pages 897-908.
    7. Zhu, Zhenxia & Zhang, Fujun & Li, Changjiang & Wu, Taotao & Han, Kai & Lv, Jianguo & Li, Yunlong & Xiao, Xuelian, 2015. "Genetic algorithm optimization applied to the fuel supply parameters of diesel engines working at plateau," Applied Energy, Elsevier, vol. 157(C), pages 789-797.

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