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An ANN-GA Framework for Optimal Engine Modeling

Author

Listed:
  • Khaldoun K. Tahboub
  • Mahmoud Barghash
  • Mazen Arafeh
  • Osama Ghazal

Abstract

Internal combustion engines are a main power source for vehicles. Improving the engine power is important which involved optimizing combustion timing and quantity of fuel. Variable valve timing (VVT) can be used in this respect to increase peak torque and power. In this work Artificial Neural Network (ANN) is used to model the effect of the VVT on the power and genetic algorithm (GA) as an optimization technique to find the optimal power setting. The same proposed technique can be used to improve fuel economy or a balanced combination of both fuel and power. Based on the findings of this work, it was noticed that the VVT setting is more important at high speed. It was also noticed that optimal power can be obtained by changing the VVT settings as a function of speed. Also to reduce computational time in obtaining the optimal VVT setting, an ANN was successfully used to model the optimal setting as a function of speed.

Suggested Citation

  • Khaldoun K. Tahboub & Mahmoud Barghash & Mazen Arafeh & Osama Ghazal, 2016. "An ANN-GA Framework for Optimal Engine Modeling," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-8, March.
    • Handle: RePEc:hin:jnlmpe:6180758
    DOI: 10.1155/2016/6180758
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